Methods for diagnosing, detecting, or assessing cancer using isotope analysis

Abstract

By adapting isotopic fingerprinting to the human body, experimental data revealed a specific chemical fingerprint of cancer, such as prostate cancer, "fossilized" in samples such as a patient's hair or nails. To interpret the isotopic chemical data of the samples, a predictive model was developed using machine learning algorithms and several dimensions to enable population differentiation. In several aspects, this provides a non-invasive method for cancer diagnosis, detection, or assessment developed by identifying the chemical fingerprint of cancer. TIFF2026509162000028.tif53159

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to U.S. Provisional Patent Application No. 63 / 446,785, filed on 17 February 2023, the contents of which Provisional Patent Application are incorporated herein by reference in their entirety for all purposes.

[0002] field This disclosure relates to a non-invasive method for diagnosing, detecting, or assessing cancer, developed in some aspects by identifying the chemical fingerprint of cancer. By adapting isotopic fingerprinting to the human body, experimental data revealed a specific chemical fingerprint of cancer, such as prostate cancer, "fossilized" in a sample such as a patient's hair or nail. To interpret the isotopic chemical data of the sample, we developed a predictive model that enables population differentiation in a high-dimensional manner, for example, by using machine learning algorithms. [Background technology]

[0003] background Cancer is one of the leading causes of death worldwide, with nearly 10 million deaths in 2020.[1] The mortality rate from cancer decreases when cases are detected and treated early. There is great interest in research and development of technologies that can accelerate the early detection and diagnosis of cancer. There are many approaches to diagnosing cancer, including health checkups, laboratory tests, imaging, or biopsies.[2] Each of these diagnostic methods has its own pitfalls. Diagnoses may be insufficiently sensitive or accurate, and may require invasive surgery, expensive equipment, or be extremely costly and time-consuming. Non-invasive and accurate diagnoses could be powerful tools in cancer screening and monitoring. For example, prostate cancer is typically screened for by medical history and health checkups or blood tests for prostate-specific antigen (PSA).[3] The results of these preliminary tests (e.g., high PSA) may then necessitate a prostate biopsy for further verification.[3] Of those who undergo biopsies, only about 25% are diagnosed with cancer, reflecting the fact that the sensitivity and specificity of PSA are insufficient, resulting in hundreds of thousands of unnecessary and expensive invasive procedures each year.

[34]

[0004] Accurate, non-invasive, or minimally invasive diagnostic techniques offer significant advantages and overcome the limitations presented by invasive diagnostic procedures. However, to date, no non-invasive diagnostic technique has been developed that can reliably detect or assess cancer while simultaneously identifying its type (e.g., pancreatic cancer, colon cancer, etc.). The ongoing need to develop non-invasive cancer diagnostic techniques remains. [Overview of the project]

[0005] overview This specification provides a method for detecting, diagnosing, or assessing cancer in a subject. The method provided is non-invasive and allows for highly reliable detection or assessment of cancer in the subject. Therefore, in some aspects, a non-invasive method for diagnosing, detecting, or assessing cancer is provided, developed by identifying the chemical fingerprint of cancer.

[0006] In this specification, in some embodiments, methods are provided for detecting whether a subject has cancer. In other embodiments, methods are provided for detecting cancer in a subject.

[0007] In any several embodiments, the method includes a step of determining the value of each of at least one isotopic feature for a sample from which the subject originates. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in the sample of the subject, a chemical feature determined from the total concentration of the isotope, the concentration of the isotope, or a chemical feature determined from the concentration of the isotope. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, an isotope concentration, an isotope concentration of the isotope, or any of the above. In any several embodiments, the at least one isotope feature includes the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject.

[0008] In any several embodiments, the method includes inputting the values ​​of each of the at least one isotopic features of the subject into a predictive model. In any several embodiments, the predictive model is configured to predict the presence or absence of cancer in the subject. In any several embodiments, the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features of a plurality of reference samples. In any several embodiments, the plurality of reference samples originate from a plurality of reference subjects.

[0009] In any several embodiments, the method includes using the predictive model to determine whether a subject has cancer. In any several embodiments, the method includes using the predictive model to detect cancer in a subject.

[0010] Furthermore, in certain embodiments of this specification, a method for detecting whether a subject has cancer is provided, comprising: (a) determining the value of each of at least one isotopic feature for a sample from the subject, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample of the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; (b) inputting the value of each of the at least one isotopic feature for the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) determining whether the subject has cancer using the predictive model.

[0011] Furthermore, in certain embodiments of this specification, a method for detecting whether a subject has cancer is provided, comprising: (a) determining the value of each of at least one isotopic feature for a sample from the subject, wherein each of the at least one isotopic feature values ​​is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3) measured in the sample of the subject; (b) inputting the values ​​of each of the at least one isotopic feature for the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model comprises at least one machine learning algorithm trained with each of the values ​​of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) determining whether the subject has cancer using the predictive model. Furthermore, in certain embodiments of this specification, a method for detecting cancer in a subject is provided, comprising: (a) determining the value of each of at least one isotopic feature for a sample from the subject, wherein each of the at least one isotopic feature values ​​is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3) measured in the sample of the subject; (b) inputting the values ​​of each of the at least one isotopic feature of the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model comprises at least one machine learning algorithm trained with each of the values ​​of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) detecting the cancer in the subject using the predictive model.

[0012] Furthermore, in some embodiments of this specification, methods for assessing the characteristics of cancer in a subject are provided.

[0013] In any several embodiments, the method includes a step of determining the value of each of at least one isotopic feature for a sample from which the subject originates. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in the sample of the subject, a chemical feature determined from the total concentration of the isotope, the concentration of the isotope, or a chemical feature determined from the concentration of the isotope. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an element, the concentration of an isotope, the concentration of an isotope of the isotope, or a normalized value thereof. In any several embodiments, the at least one isotope feature includes the concentration of an element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope of the isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope of the isotope measured in the sample of the subject.

[0014] In any several embodiments, the method includes inputting the values ​​of each of the at least one isotopic features of the subject into a predictive model. In any several embodiments, the predictive model is configured to predict cancer characteristics in the subject. In any several embodiments, the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features of a plurality of reference samples. In any several embodiments, the plurality of reference samples originate from a plurality of reference subjects.

[0015] In any and all embodiments, the method includes using the predictive model to determine the characteristics of cancer in the subject.

[0016] Furthermore, in certain embodiments of this specification, a method is provided for assessing cancer characteristics in a subject, comprising: (a) determining the value of each of at least one isotopic feature for a sample from the subject, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample of the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; (b) inputting the value of each of the at least one isotopic feature of the subject into a predictive model configured to predict cancer characteristics in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) determining cancer characteristics in the subject using the predictive model.

[0017] Furthermore, in certain embodiments of this specification, a method for assessing cancer characteristics in a subject is provided, comprising: (a) determining the value of each of at least one isotopic feature for a sample from the subject, wherein each of the at least one isotopic feature values ​​is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3) measured in the sample of the subject; (b) inputting the values ​​of each of the at least one isotopic feature of the subject into a predictive model configured to predict cancer characteristics in the subject, wherein the predictive model comprises at least one machine learning algorithm trained with each of the values ​​of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) determining cancer characteristics in the subject using the predictive model.

[0018] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0019] In any several embodiments, determining the value of each of the at least one isotopic features includes mass spectrometry analysis. In any several embodiments, the value is a normalized value, and determining the value of each of the at least one isotopic features includes calculating the normalized value from mass spectrometry analysis.

[0020] In some arbitrary embodiments, the mass spectrometry analysis is of a liquid sample of the sample. In some arbitrary embodiments, the sample is a solid sample and the mass spectrometry analysis is of a liquid sample of the solid sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing a liquid sample of the sample for analysis by mass spectrometry. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing a liquid sample of the solid sample for analysis by mass spectrometry. In some arbitrary embodiments, the liquid sample is prepared by digestion of the sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of the solid sample.

[0021] In some arbitrary embodiments, the mass spectrometry analysis is of a solid sample of the specimen. In some arbitrary embodiments, the specimen is a solid specimen, and the mass spectrometry analysis is of the solid specimen. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing a solid specimen of the specimen for analysis by mass spectrometry. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing a solid specimen. In some arbitrary embodiments, the solid specimen is analyzed via combustion.

[0022] In any several embodiments, determining the value of each of the at least one isotopic features involves mass spectrometry analysis of a liquid or solid sample of the sample. In any several embodiments, the sample is a solid sample, and determining the value of each of the at least one isotopic features involves mass spectrometry analysis of a liquid or solid sample of the solid sample.

[0023] In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of a liquid sample prepared by digestion of the sample or a solid sample analyzed via combustion of the sample. In some arbitrary embodiments, the sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of the solid sample or a liquid sample prepared by digestion of the solid sample. In some embodiments, the solid sample is analyzed via combustion.

[0024] In any several embodiments, determining the value of each of the at least one isotopic feature includes (i) preparing a liquid sample of the sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and determining the value of each of the at least one isotopic feature comprises (i) preparing a liquid sample of the solid sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the solid sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of the element and isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the element and isotopes to create a value for each of the at least one isotopic feature.

[0025] In any several embodiments, determining the value of each of the at least one isotopic feature comprises (i) preparing a solid sample of the sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared solid sample, the detection of which includes combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and determining the value of each of the at least one isotopic feature comprises (i) preparing the solid sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of the element and isotopes present in the prepared solid sample, optionally including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the element and isotopes to create a value for each of the at least one isotopic feature.

[0026] In some arbitrary embodiments, the decision in step (a), the input in step (b), and / or the decision in step (c) are made by the processor of the computing device. In some arbitrary embodiments, the decision in step (a) is made by the processor of the computing device. In some arbitrary embodiments, the input in step (b) is made by the processor of the computing device. In some arbitrary embodiments, the decision in step (c) is made by the processor of the computing device. In some arbitrary embodiments, the decision in step (a), the input in step (b), and the decision in step (c) are made by the processor of the computing device.

[0027] Furthermore, in certain embodiments of this specification, a method for detecting whether a subject has cancer is provided, comprising: (a) a step of receiving values ​​for each of at least one isotopic features for a sample from the subject using one or more processors, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample from the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; (b) a step of inputting the values ​​for each of the at least one isotopic features of the subject into a predictive model configured to predict the presence or absence of cancer in the subject using at least one machine learning algorithm trained with the values ​​for each of the at least one isotopic features from a plurality of reference samples from a plurality of reference subjects; and (c) a step of determining whether the subject has cancer using the predictive model.

[0028] Furthermore, in certain embodiments of this specification, a method for detecting whether a subject has cancer is provided, comprising: (a) a step of receiving values ​​for each of at least one isotopic features for a sample from the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) a step of inputting each of the values ​​for the at least one isotopic features of the subject into a predictive model configured to predict the presence or absence of cancer in the subject using at least one machine learning algorithm trained with each of the values ​​for the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) a step of determining whether the subject has cancer using the predictive model.

[0029] Furthermore, in some embodiments of this specification, a method for detecting cancer in a subject is provided, including: (a) a step of receiving values ​​for each of at least one isotopic features for a sample from the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) a step of inputting the values ​​for each of the at least one isotopic features of the subject into a predictive model configured to predict the presence or absence of cancer in the subject using at least one machine learning algorithm trained with each of the values ​​for the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) a step of detecting the cancer in the subject using the predictive model.

[0030] Furthermore, in some embodiments herein, a method is provided for assessing cancer characteristics in a subject, including: (a) a step of receiving values ​​for each of at least one isotopic features for a sample from the subject using one or more processors, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample of the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope of an isotope, or a chemical feature determined from the concentration of an isotope of an isotope; (b) a step of inputting the values ​​for each of the at least one isotopic features of the subject into a predictive model configured to predict cancer characteristics in the subject using at least one machine learning algorithm trained with values ​​for each of the at least one isotopic features from a plurality of reference samples from a plurality of reference subjects; and (c) a step of determining cancer characteristics in the subject using the predictive model.

[0031] Furthermore, in some embodiments of this specification, a method for assessing cancer characteristics in a subject is provided, including: (a) a step of receiving values ​​for each of at least one isotopic features for a sample from the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) a step of inputting the values ​​for each of the at least one isotopic features of the subject into a predictive model configured to predict cancer characteristics in the subject using at least one machine learning algorithm trained with each of the values ​​for the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (c) a step of determining cancer characteristics in the subject using the predictive model.

[0032] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0033] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis. In some arbitrary embodiments, the value is a normalized value, and determining the value of each of the at least one isotopic feature includes calculating the normalized value from mass spectrometry analysis.

[0034] In some arbitrary embodiments, the mass spectrometry analysis is performed on a liquid sample of the sample. In some arbitrary embodiments, the sample is a solid sample and the mass spectrometry analysis is performed on a liquid sample of the solid sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of the sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of a solid sample.

[0035] In some arbitrary embodiments, the mass spectrometry analysis is performed on a solid sample. In some arbitrary embodiments, the sample is a solid sample, and the mass spectrometry analysis is performed on the solid sample. In some arbitrary embodiments, the solid sample is analyzed by combustion.

[0036] In some of the embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample of the sample or a solid sample of the sample. In some of the embodiments, the sample is a solid sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of the solid sample or a liquid sample of the solid sample.

[0037] In some of the embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of the sample or a solid sample analyzed via combustion of the sample. In some of the embodiments, the sample is a solid sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of the solid sample or a liquid sample prepared by digestion of the solid sample. In some of the embodiments, the solid sample is analyzed via combustion.

[0038] In any several embodiments, the method includes a step prior to step (a) of determining the value of each of the at least one isotopic feature.

[0039] In any several embodiments, prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing a liquid sample of the sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing a liquid sample of the solid sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the solid sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of elemental isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the element and isotopes to create a value for each of the at least one isotopic feature.

[0040] In any several embodiments, prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing a solid sample of the sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared solid sample, the detection including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing the solid sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of the element and isotopes present in the prepared solid sample, optionally including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the element and isotopes to create a value for each of the at least one isotopic feature.

[0041] In any several embodiments, the analysis of the detected one or more ionized products of an isotope is accompanied by a reference to one or more ionized products of the element present in a standard material. In any several embodiments, the analysis of the detected one or more ionized products of an element and its isotope is accompanied by a reference to one or more ionized products of the element and its isotope present in a standard material.

[0042] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of the element present in a standard material comprising multiple calibration standards and blank standards. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of the element and isotope present in a standard material comprising multiple calibration standards and blank standards.

[0043] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard and / or a reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including a reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard and a reference material.

[0044] In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and / or a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and a reference material.

[0045] In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of the elements present in the standard substance and the prepared liquid or solid sample. In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of the elements and isotopes present in the standard substance and the prepared liquid or solid sample.

[0046] Furthermore, in some embodiments of this specification, a method is provided for detecting whether a subject has cancer, including: (a) preparing a sample to be obtained from the subject; (b) preparing a liquid or solid sample from the sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the sample; (c) preparing a standard material comprising a plurality of calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionized products of elements present in the standard material and in the prepared liquid or solid sample, respectively; and (e) analyzing the detected one or more ionized products of the isotopes to create a value for each of at least one isotopic feature. (f) a step of receiving each of the at least one isotope feature, wherein the at least one isotope feature includes the total concentration of an isotope, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope of an isotope, or a chemical feature determined from the concentration of an isotope of an isotope; (g) a step of using one or more processors to receive each of the at least one isotope feature values ​​into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model includes at least one machine learning algorithm trained with each of the values ​​of the at least one isotope feature from a plurality of reference samples from a plurality of reference subjects; and (h) a step of using the predictive model to determine whether the subject has the cancer.

[0047] Furthermore, in some embodiments herein, a method is provided for detecting whether a subject has cancer, including: (a) preparing a solid sample to be obtained from the subject; (b) preparing a liquid sample or the solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) preparing a standard material comprising a plurality of calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionized products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) determining the value of each of at least one isotopic feature, by selecting the detected one or more of the elements and isotopes. (1) a step of analyzing a plurality of ionization products, wherein the value of each of the at least one isotopic feature is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) a step of receiving the value of each of the at least one isotopic feature in one or more processors; (g) a step of using at least one processor to input the value of each of the at least one isotopic feature into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model comprises at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) a step of using the predictive model to determine whether the subject has the cancer.

[0048] Furthermore, in some embodiments herein, a method for detecting cancer in a subject is provided, including: (a) preparing a solid sample to be obtained from the subject; (b) preparing a liquid sample or the solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) preparing a standard material comprising a plurality of calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionized products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) determining the value of each of at least one isotopic feature, wherein the detected one or (1) a step of analyzing a plurality of ionization products, wherein the value of each of the at least one isotopic feature is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) receiving the value of each of the at least one isotopic feature in one or more processors; (g) using at least one processor to input the value of each of the at least one isotopic feature into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model comprises at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) detecting the cancer in the subject using the predictive model.

[0049] Furthermore, in some embodiments herein, a method is provided for assessing cancer characteristics in a subject, including: (a) preparing a sample to be obtained from the subject; (b) preparing a liquid or solid sample from the sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the sample; (c) preparing a standard material comprising a plurality of calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample separately into a mass spectrometer to detect one or more ionized products of elements present in the standard material and the prepared liquid or solid sample, respectively; (e) creating a value for each of at least one isotopic feature, the detected one or more ionized products of the isotopes (f) a step of analyzing the at least one isotopic feature, wherein the at least one isotopic feature includes the total concentration of the isotope, a chemical feature determined from the total concentration of the isotope, the concentration of an isotope of the isotope, or a chemical feature determined from the concentration of an isotope of the isotope; (g) a step of receiving the respective values ​​of the at least one isotopic feature in one or more processors; (h) a step of using at least one processor to input the respective values ​​of the at least one isotopic feature into a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the respective values ​​of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) a step of using the predictive model to determine cancer characteristics in the subject.

[0050] Furthermore, in some embodiments herein, a method is provided for assessing cancer characteristics in a subject, including: (a) preparing a solid sample to be obtained from the subject; (b) preparing a liquid sample or the solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) preparing a standard material comprising a plurality of calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample separately into a mass spectrometer to detect one or more ionized products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) creating a value for each of at least one isotopic feature, wherein the detected one of the elements and isotopes A step of analyzing one or more ionization products, wherein the value of each of the at least one isotopic feature is (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) receiving the value of each of the at least one isotopic feature in one or more processors; (g) using at least one processor to input the value of each of the at least one isotopic feature into a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) determining cancer characteristics in the subject using the predictive model.

[0051] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0052] In some arbitrary embodiments, the sample comprises keratinous tissue, optionally hard keratinous tissue. In some arbitrary embodiments, the sample comprises keratinous tissue. In some arbitrary embodiments, the sample comprises hard keratinous tissue.

[0053] In some arbitrary aspects, the sample is a non-invasive sample, optionally a hair sample or a nail sample. In some arbitrary aspects, the sample is a non-invasive sample. In some arbitrary aspects, the sample is a hair sample or a nail sample. In some arbitrary aspects, the sample is a hair sample. In some arbitrary aspects, the sample is a nail sample.

[0054] In any given aspect, digestion is by acid digestion and / or thermal digestion. In any given aspect, digestion is by acid digestion and thermal digestion.

[0055] In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3). In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3) and hydrogen peroxide (H2O2).

[0056] In some arbitrary aspects, thermal digestion is performed by microwaves.

[0057] In some arbitrary embodiments, the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS). In some arbitrary embodiments, the ICP-MS is performed using kinetic energy discrimination (KED).

[0058] In any several embodiments, the at least one isotope feature includes atomic percentage, isotope ratio, abundance, delta, total concentration and / or concentration ratio. In any several embodiments, the at least one isotope feature includes atomic percentage. In any several embodiments, the at least one isotope feature includes isotope ratio. In any several embodiments, the at least one isotope feature includes abundance. In any several embodiments, the at least one isotope feature includes delta. In any several embodiments, the at least one isotope feature includes total concentration. In any several embodiments, the at least one isotope feature includes concentration ratio.

[0059] In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the abundance of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the delta of at least one isotope of the isotope.

[0060] In any several embodiments, the at least one isotopic feature is a plurality of isotopic features, each independently selected from the group consisting of atomic percentage, isotopic ratio, abundance, delta, total concentration, and concentration ratio.

[0061] In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the abundance of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the delta of at least one isotope of the isotope.

[0062] In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element, a normalized value of the concentration of an isotope, or a normalized value of the concentration of an isotope of the isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope of the isotope.

[0063] In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%), an abundance (F), an isotope ratio (R), a concentration ratio (CR), or a delta value (delta), or any combination of the aforementioned. In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an abundance (F). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an isotope ratio (R). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a concentration ratio (CR). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a delta value (delta).

[0064] In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In some arbitrary embodiments, the delta value is a conventional delta value, a study-specific delta value (InH), a non-similar simple- nitrogen-based delta value (simple Nδ), or a non-similar pseudo- nitrogen-based delta value (pseudo Nδ). In some arbitrary embodiments, the delta value is a conventional delta value. In some arbitrary embodiments, the delta value is a study-specific delta value (InH). In some arbitrary embodiments, the delta value is a non-similar simple- nitrogen-based delta value (simple Nδ). In some arbitrary embodiments, the delta value is a non-similar pseudo- nitrogen-based delta value (pseudo Nδ).

[0065] In any given embodiment, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S.

[0066] In some arbitrary embodiments, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In some arbitrary embodiments, the isotope is Ag. In some arbitrary embodiments, the isotope is Al. In some arbitrary embodiments, the isotope is B. In some arbitrary embodiments, the isotope is Ba. In some arbitrary embodiments, the isotope is Br. In some arbitrary embodiments, the isotope is Ca. In some arbitrary embodiments, the isotope is Co. In some arbitrary embodiments, the isotope is Cr. In some arbitrary embodiments, the isotope is Cu. In some arbitrary embodiments, the isotope is Fe. In some arbitrary embodiments, the isotope is Ge. In some arbitrary embodiments, the isotope is Hg. In some arbitrary aspects, the isotope is I. In some arbitrary aspects, the isotope is K. In some arbitrary aspects, the isotope is Li. In some arbitrary aspects, the isotope is Mg. In some arbitrary aspects, the isotope is Mn. In some arbitrary aspects, the isotope is Mo. In some arbitrary aspects, the isotope is Na. In some arbitrary aspects, the isotope is Ni. In some arbitrary aspects, the isotope is Pb. In some arbitrary aspects, the isotope is Rb. In some arbitrary aspects, the isotope is Ru. In some arbitrary aspects, the isotope is Se. In some arbitrary aspects, the isotope is Sn. In some arbitrary aspects, the isotope is Sr. In some arbitrary aspects, the isotope is Te. In some arbitrary aspects, the isotope is Ti. In some arbitrary aspects, the isotope is U. In some arbitrary aspects, the isotope is V. In some arbitrary embodiments, the isotope is Y. In some arbitrary embodiments, the isotope is Zn.

[0067] In any of several embodiments, the isotope is N, C, S, or O. In any of several embodiments, the isotope is N. In any of several embodiments, the isotope is C. In any of several embodiments, the isotope is S. In any of several embodiments, the isotope is O.

[0068] In any of several embodiments, the isotope is Mo, Cr, Te, C, or Hg. In any of several embodiments, the isotope is Mo. In any of several embodiments, the isotope is Cr. In any of several embodiments, the isotope is Te. In any of several embodiments, the isotope is C. In any of several embodiments, the isotope is Hg.

[0069] In any of several embodiments, the at least one isotope feature is one isotope feature. In any of several embodiments, the isotope of the at least one isotope feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 Cr. In any of several embodiments, the isotope of the at least one isotope feature is 66 including Zn. In any of several embodiments, the isotope of the at least one isotope feature is 64 including Zn. In any of several embodiments, the isotope of the at least one isotope feature is 68 including Zn. In any of several embodiments, the isotope of the at least one isotope feature is 87 including Sr. In any of several embodiments, the isotope of the at least one isotope feature is 88 including Sr. In any of several embodiments, the isotope of the at least one isotope feature is 86 including Sr. In any of several embodiments, the isotope of the at least one isotope feature is 47Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 48 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 46 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 50 Contains Cr. In any several embodiments, the isotope of at least one isotopic feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti and 50 Contains Cr.

[0070] In some arbitrary embodiments, the at least one isotope feature is a plurality of isotope features. In some arbitrary embodiments, the plurality of isotope features is about 2 to about 100 isotope features. In some arbitrary embodiments, the plurality of isotope features is 2 to 100 isotope features. In any several embodiment, the plurality of isotopic features include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 or more isotopic features. In any several embodiment, the plurality of isotopic features include at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopic features.

[0071] In any and all embodiments, the at least one isotopic feature is a plurality of isotopic features determined from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or more elements or isotopes. In any several embodiment, the plurality of isotopic features are determined from at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes.

[0072] In any several embodiments, the plurality of isotopic features include abundance and delta. In any several embodiments, abundance and delta values ​​are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of the isotope.

[0073] In any several embodiments, the plurality of isotopic features include abundance, atomic percentage, and / or isotopic ratio. In any several embodiments, abundance, atomic percentage, and / or isotopic ratios are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of an isotope.

[0074] In any several embodiments, the isotopes include Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, and / or S. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Na, Mg, Al, P, K, Sc, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, I, Te, Ba, Hg, Pb, U, C, N, O, or S, or any combination of the aforementioned. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S, or any combination of the aforementioned. In any several embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Mg, Ca, Ti, Cr, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Mo, Ag, Sn, Ba, Hg, Pb, U, C, N, S, or any combination of the foregoing.

[0075] In any and some embodiments, the isotope contains Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In any and some embodiments, the isotope contains Ag. In any and some embodiments, the isotope contains Al. In any and some embodiments, the isotope contains B. In any and some embodiments, the isotope contains Ba. In any and some embodiments, the isotope contains Br. In any and some embodiments, the isotope contains Ca. In any and some embodiments, the isotope contains Co. In any and some embodiments, the isotope contains Cr. In any and some embodiments, the isotope contains Cu. In any and some embodiments, the isotope contains Fe. In any and some embodiments, the isotope contains Ge. In any and some embodiments, the isotope contains Hg. In some arbitrary embodiments, the isotope contains I. In some arbitrary embodiments, the isotope contains K. In some arbitrary embodiments, the isotope contains Li. In some arbitrary embodiments, the isotope contains Mg. In some arbitrary embodiments, the isotope contains Mn. In some arbitrary embodiments, the isotope contains Mo. In some arbitrary embodiments, the isotope contains Na. In some arbitrary embodiments, the isotope contains Ni. In some arbitrary embodiments, the isotope contains Pb. In some arbitrary embodiments, the isotope contains Rb. In some arbitrary embodiments, the isotope contains Ru. In some arbitrary embodiments, the isotope contains Se. In some arbitrary embodiments, the isotope contains Sn. In some arbitrary embodiments, the isotope contains Sr. In some arbitrary embodiments, the isotope contains Te. In some arbitrary embodiments, the isotope contains Ti. In some arbitrary embodiments, the isotope contains U. In some arbitrary embodiments, the isotope contains V. In some arbitrary embodiments, the isotope contains Y. In some arbitrary embodiments, the isotope contains Zn. In some arbitrary embodiments, the isotopes contain Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, and Zn.

[0076] In some arbitrary embodiments, the isotope contains N, C, S, and / or O. In some arbitrary embodiments, the isotope contains N. In some arbitrary embodiments, the isotope contains C. In some arbitrary embodiments, the isotope contains S. In some arbitrary embodiments, the isotope contains O. In some arbitrary embodiments, the isotope contains N, C, S, and O.

[0077] In any and some embodiments, the isotopes include Mo, Cr, Te, C and / or Hg. In any and some embodiments, the isotopes include Mo. In any and some embodiments, the isotopes include Cr. In any and some embodiments, the isotopes include Te. In any and some embodiments, the isotopes include C. In any and some embodiments, the isotopes include Hg. In any and some embodiments, the isotopes include Mo, Cr, Te, C and Hg.

[0078] In some arbitrary embodiments, at least one isotope feature includes a concentration ratio. In some arbitrary embodiments, the concentration ratio is N / C, S / C, O / C, S / N, N / O, or S / O. In some arbitrary embodiments, the concentration ratio is N / C. In some arbitrary embodiments, the concentration ratio is S / C. In some arbitrary embodiments, the concentration ratio is O / C. In some arbitrary embodiments, the concentration ratio is S / N. In some arbitrary embodiments, the concentration ratio is N / O. In some arbitrary embodiments, the concentration ratio is S / O.

[0079] In any several embodiments, the plurality of isotope features include at least one concentration ratio. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and / or S / O. In any several embodiments, the at least one concentration ratio includes N / C. In any several embodiments, the at least one concentration ratio includes S / C. In any several embodiments, the at least one concentration ratio includes O / C. In any several embodiments, the at least one concentration ratio includes S / N. In any several embodiments, the at least one concentration ratio includes N / O. In any several embodiments, the at least one concentration ratio includes S / O. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and S / O.

[0080] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 Abundance of C (F), atomic percentage (atomic %) or isotopic ratio (R) (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (F34S), relative to stable isotopes of N 15 The present invention includes at least one, two, three, four, five, six, seven, eight, nine, or ten isotopic features from among the abundance, atomic percentage, or isotopic ratio of N (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO). In any several embodiments, the at least one isotopic feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34Abundance of S, atomic percentage or isotopic ratio (F34S), relative to stable isotopes of N 15 This includes the abundance of N, atomic percentage or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0081] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio (F13C) of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of O. 18 This includes the abundance, atomic percentage, or isotopic ratio (f18O) of O. In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to nitrogen (SdN). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio (F34S) of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of N. 15 This includes the abundance, atomic percentage, or isotopic ratio of N (F15N). In any several embodiments, the at least one isotopic feature includes the concentration ratio of oxygen to carbon (OdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to oxygen (NdO). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to carbon (SdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to oxygen (SdO).

[0082] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (F34S), relative to stable isotopes of N 15 This includes the abundance of N, atomic percentage or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0083] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn, 67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and98 includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more of the abundance ratio, atomic percentage or isotope ratio of each isotope of Mo to the stable isotope of the same element.

[0084] In any of various embodiments, the at least one isotope feature is the 101 abundance ratio, atomic percentage or isotope ratio of Ru to the stable isotope of Ru. In any of various embodiments, the at least one isotope feature is the 107 abundance ratio, atomic percentage or isotope ratio of Ag to the stable isotope of Ag. In any of various embodiments, the at least one isotope feature includes the abundance ratio, atomic percentage or isotope ratio of 109Ag to the stable isotope of Ag. In any of various embodiments, the at least one isotope feature is the 10 abundance ratio, atomic percentage or isotope ratio of B to the stable isotope of B. In any of various embodiments, the at least one isotope feature is the 118 abundance ratio, atomic percentage or isotope ratio of Sn to the stable isotope of Sn. In any of various embodiments, the at least one isotope feature is the 119 abundance ratio, atomic percentage or isotope ratio of Sn to the stable isotope of Sn. In any of various embodiments, the at least one isotope feature is the 124 abundance ratio, atomic percentage or isotope ratio of Sn to the stable isotope of Sn. In any of various embodiments, the at least one isotope feature is the 124 abundance ratio, atomic percentage or isotope ratio of Te to the stable isotope of Te. In any of various embodiments, the at least one isotope feature is the 127 abundance ratio, atomic percentage or isotope ratio of I to the stable isotope of I. In any of various embodiments, the at least one isotope feature is the 128 abundance ratio, atomic percentage or isotope ratio of Te to the stable isotope of Te. In any of various embodiments, the at least one isotope feature is the abundance ratio, atomic percentage or isotope ratio of Ba to the stable isotope of Ba136 It includes the abundance ratio, atomic percentage or isotope ratio of Ba. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Ba 137 It includes the abundance ratio, atomic percentage or isotope ratio of Ba. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of C 13 It includes the abundance ratio, atomic percentage or isotope ratio of C. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of U 235 It includes the abundance ratio, atomic percentage or isotope ratio of U. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of U 238 It includes the abundance ratio, atomic percentage or isotope ratio of U. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Na 23 It includes the abundance ratio, atomic percentage or isotope ratio of Na. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Al 27 It includes the abundance ratio, atomic percentage or isotope ratio of Al. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of S 34 It includes the abundance ratio, atomic percentage or isotope ratio of S. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of K 39 It includes the abundance ratio, atomic percentage or isotope ratio of K. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Ca 44 It includes the abundance ratio, atomic percentage or isotope ratio of Ca. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Ti 48 It includes the abundance ratio, atomic percentage or isotope ratio of Ti. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Cr 54 It includes the abundance ratio, atomic percentage or isotope ratio of Cr. In any of several embodiments, the at least one isotope feature is relative to the stable isotope(s) of Fe 54This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ni. 62 This includes the abundance, atomic percentage, or isotopic ratio of Ni. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Cu. 63 This includes the abundance, atomic percentage, or isotopic ratio of Cu. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 66 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 The abundance, atomic percentage, or isotopic ratio of Zn is included. In any several embodiments, the at least one isotopic feature includes the abundance, atomic percentage, or isotopic ratio of 68Zn relative to the stable isotope of Zn. In any several embodiments, the at least one isotopic feature includes relative to the stable isotope of Ge 70 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ge. 74 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 76 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 77 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Br. 79This includes the abundance, atomic percentage, or isotopic ratio of Br. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 80 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 82 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Y. 89 The abundance, atomic percentage, or isotopic ratio of Y is included. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 98 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0085] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn,67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and 98 This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0086] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al, 34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 Each of the isotopes of Mo contains at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more isotopic ratios, atomic percentages, or isotopic ratios of the isotope relative to its stable isotope.

[0087] In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 107 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 109 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Sn. 118 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127This includes the abundance, atomic percentage, or isotopic ratio of I. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 235 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Al. 27 This includes the abundance, atomic percentage, or isotopic ratio of Al. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0088] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al,34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0089] In any several embodiments, the at least one isotope feature is relative to the stable isotope of I. 127 The abundance of I includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 Abundance of I, atomic percentage or isotopic ratio, and relative to stable isotopes of Ca 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca.

[0090] In any several embodiments, the isotope of the at least one isotope feature is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn,77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 Includes U, or any combination of the aforementioned.

[0091] In any and some embodiments, the isotope of the at least one isotope feature contains Zn. In any and some embodiments, the isotope of the at least one isotope feature is 64 Zn or 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Contains Zn. The isotope of at least one isotopic feature is 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Zn and 66 Contains Zn.

[0092] In any several embodiments, the isotope of the at least one isotope feature contains S. In any several embodiments, the isotope of the at least one isotope feature is 34 Includes S.

[0093] In any and some embodiments, the isotope of the at least one isotope feature includes N, C, S, or O. In any and some embodiments, the isotope of the at least one isotope feature includes N. In any and some embodiments, the isotope of the at least one isotope feature includes C. In any and some embodiments, the isotope of the at least one isotope feature includes S. In any and some embodiments, the isotope of the at least one isotope feature includes O. In any and some embodiments, the isotopes of the at least one isotope feature include N, C, S, and O.

[0094] In some arbitrary manner, a machine learning algorithm is a supervised machine learning algorithm.

[0095] In some arbitrary aspects, the predictive model is a classification model. In some arbitrary aspects, the predictive model is a binary classification model. In some arbitrary aspects, the predictive model is a multiclass classification model.

[0096] In some arbitrary aspects, the machine learning algorithm is an ensemble algorithm that includes logistic regression (e.g., ridge, lasso, or elastic net), naive Bayes classifier, Gaussian NB, Gaussian NB isotonic, Gaussian NB sigmoid, random forest classifier, gradient boosting classifier, XG boosted classifier, histogram gradient boosting classifier, deep neural network classifier, decision tree classifier, bagging classifier, extra tree classifier, Ada Boost classifier, Cat Boost classifier, LGBM classifier, support vector classifier, perceptron classifier (e.g., multilayer perceptron classifier), deep learning classifier for tabular data (e.g., TabNet or TabML), passive-aggressive classifier or SGD classifier, or any combination of the aforementioned.

[0097] In some arbitrary aspects, the machine learning algorithm is a support vector classifier, a decision tree-based ensemble classifier, or a multilayer perceptron classifier.

[0098] In some arbitrary manner, the predictive model is a regression model.

[0099] In some arbitrary manner, the machine learning algorithm is an unsupervised machine learning algorithm.

[0100] In some arbitrary embodiments, the predictive model is a general model. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, and / or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area compared to another of the multiple reference subjects. In some arbitrary embodiments, the multiple reference subjects originate from at least two, three, four, five, six or more different countries. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different sex compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different age compared to another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects belongs to a different cultural or ethnic group than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different dietary habit than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different type of cancer than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different dietary habit, and has a different type of cancer than another of the multiple reference subjects.

[0101] In some arbitrary aspects, the predictive model is a specialized model. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and / or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area. In some arbitrary aspects, each of the multiple reference subjects is of the same sex. In some arbitrary aspects, each of the multiple reference subjects is male. In some arbitrary aspects, each of the multiple reference subjects is female. In some arbitrary aspects, each of the multiple reference subjects is of the same age. In some arbitrary aspects, each of the multiple reference subjects has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and has the same type of cancer.

[0102] In some arbitrary aspects, the subject is a mammal. In some arbitrary aspects, the subject is a human.

[0103] In any and some aspects, the cancer is a blood cancer or a solid tumor. In any and some aspects, the cancer is a blood cancer. In any and some aspects, the blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some aspects, the blood cancer is lymphoma. In any and some aspects, the blood cancer is multiple myeloma. In any and some aspects, the blood cancer is leukemia. In any and some aspects, the cancer is a solid tumor.

[0104] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0105] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0106] In some of its embodiments, the method is non-invasive.

[0107] In any several embodiments, the predictive model is constructed by (i) determining the value of each of the at least one isotopic feature for the plurality of reference samples; and (ii) training a machine learning algorithm using the values ​​of each of the at least one isotopic feature for the plurality of reference samples.

[0108] In some arbitrary aspects, some of the reference objects are known to have cancer. In some arbitrary aspects, it is assumed that some of the reference objects do not have cancer. In some arbitrary aspects, some of the reference objects are known to have cancer, and it is assumed that some of the reference objects do not have cancer.

[0109] In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples. In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples.

[0110] In some arbitrary embodiments, the label indicates whether the reference sample originates from a reference subject known to have cancer or from a reference subject assumed not to have cancer.

[0111] In some arbitrary aspects, the label indicates the characteristics of the reference cancer from which the acquired reference sample originates.

[0112] In some arbitrary embodiments, the label indicates the tumor burden level of the reference cancer from which the acquired reference sample originates.

[0113] In any several embodiments, the method further includes a step prior to step (a) of training a machine learning algorithm using the values ​​of each of the at least one isotopic features for the plurality of reference samples. In any several embodiments, the method further includes a step prior to step (a) of training a machine learning algorithm using the plurality of labels. In any several embodiments, the method further includes a step prior to step (a) of training a machine learning algorithm using the plurality of labels and the values ​​of each of the at least one isotopic features for the plurality of reference samples.

[0114] In any several embodiments, the method further includes a step of preprocessing each value of the at least one isotope feature before training a machine learning algorithm. In any several embodiments, the preprocessing step includes controlled imputation of missing values. In any several embodiments, the preprocessing step includes replacing missing values ​​with zeros. In any several embodiments, the preprocessing step includes normalizing the data.

[0115] In some arbitrary aspects, cancer is detected in a subject if the predictive model indicates the presence of cancer.

[0116] In any several embodiments, the accuracy of cancer prediction by the method is greater than 50%, greater than 60%, greater than 70%, greater than 80%, greater than 90%, or greater than 95%. In any several embodiments, the accuracy of cancer prediction by the method is about greater than 70%. In any several embodiments, the accuracy of cancer prediction is greater than 90%, for example, greater than 91%, greater than 92%, greater than 93%, greater than 94%, or greater than 95%.

[0117] In any several embodiments, the method further includes verification of detected cancer by a method selected from the group consisting of blood tests, urine tests, biopsies, endoscopies, lumbar punctures, Pap tests, surgeries, genetic tests, and imaging.

[0118] In some arbitrary embodiments, the method is a method for diagnosing cancer in a subject. In some arbitrary embodiments, if the predictive model indicates the presence of cancer, the subject is diagnosed with cancer. In some arbitrary embodiments, the method is a method for diagnosing cancer in a subject, and if the predictive model indicates the presence of cancer, the subject is diagnosed with cancer.

[0119] In any and some embodiments, the subject has one or more symptoms that indicate the presence of cancer in the subject.

[0120] In some arbitrary aspects, the method is a method for prophylactic screening for cancer in a subject.

[0121] In any and some embodiments, if a subject is diagnosed with cancer, the subject receives treatment for the cancer. In any and some embodiments, if a subject is diagnosed with cancer, the method further includes a step of treating the subject for the cancer.

[0122] Furthermore, in some embodiments of this specification, methods for treating cancer in a subject are provided.

[0123] In any several embodiments, the method includes diagnosing a subject with cancer according to one of the methods provided. In any several embodiments, the presence of cancer is indicated by a predictive model.

[0124] In any several embodiments, the method includes selecting subjects to be diagnosed with cancer according to one of the methods provided. In any several embodiments, the presence of the cancer is indicated by a predictive model.

[0125] In any and all embodiments, the method includes the step of treating a subject for cancer using the treatment for cancer.

[0126] Furthermore, in some embodiments of this specification, methods for treating cancer in a subject are provided, comprising the steps of (a) diagnosing the subject as having cancer according to one of the methods provided, wherein the presence of cancer is indicated by a predictive model; and (b) treating the subject for the cancer using a treatment for the cancer.

[0127] Furthermore, in some embodiments of this specification, a method for treating cancer in a subject is provided, comprising the steps of: (a) selecting a subject to be diagnosed with cancer according to one of the methods provided, wherein the presence of the cancer is indicated by a predictive model; and (b) treating the subject for the cancer using a treatment for the cancer.

[0128] In any and all embodiments, the method is a method for monitoring cancer treatment in a subject.

[0129] In some aspects, the subject has been previously diagnosed with cancer. In some aspects, the subject has been previously diagnosed with cancer and is receiving treatment, or is believed to have been previously diagnosed with cancer and is in remission. In some aspects, the subject has been previously diagnosed with cancer and is receiving treatment. In some aspects, the subject has been previously diagnosed with cancer and is believed to be in remission.

[0130] In some arbitrary aspects, if cancer is detected in a subject, cancer treatment for that subject is continued or resumed. In some arbitrary aspects, if cancer is detected in a subject, cancer treatment for that subject is continued. In some arbitrary aspects, if cancer is detected in a subject, cancer treatment for that subject is resumed.

[0131] In any and some embodiments, if cancer is detected in the subject, the method further includes a step of continuing or resuming treatment of the subject for the cancer. In any and some embodiments, if cancer is detected in the subject, the method further includes a step of continuing treatment of the subject for the cancer. In any and some embodiments, if cancer is detected in the subject, the method further includes a step of resuming treatment of the subject for the cancer.

[0132] Furthermore, in some embodiments of this specification, methods for treating cancer in a subject are provided, comprising the steps of: (a) detecting cancer in a subject according to one of the methods provided, wherein a predictive model indicates the presence of cancer, and the subject is believed to have been previously diagnosed with cancer and treated for the cancer, or to have been previously diagnosed with cancer and is in remission; and (b) treating the subject for the cancer using a treatment for the cancer.

[0133] Furthermore, in some embodiments of this specification, a method for treating cancer in a subject is provided, comprising the steps of: (a) selecting a subject for which cancer is detected according to one of the methods provided, wherein a predictive model indicates the presence of the cancer, and the subject is believed to have been previously diagnosed with the cancer and treated for the cancer, or to have been previously diagnosed with the cancer and is in remission; and (b) treating the subject for the cancer using a treatment for the cancer.

[0134] In any particular aspect, the treatment includes chemotherapy, radiotherapy, cryotherapy, photodynamic therapy, laser therapy, immunotherapy, targeted therapy, hormone therapy, surgical procedures to remove cancer, stem cell transplantation, bone marrow transplantation, or any combination of the foregoing.

[0135] In any and all embodiments, the predictive model is constructed and applied using a computer program product which is materially embodied in a non-temporary, machine-readable storage medium, and which includes instructions configured to cause one or more data processors to construct and apply the predictive model.

[0136] Furthermore, in some embodiments of this specification, a system for detecting whether a subject has cancer is provided.

[0137] In any several embodiments, the system includes one or more data processors and a non-temporary computer-readable storage medium containing instructions. In any several embodiments, when an instruction is executed in the one or more data processors, it causes the one or more data processors to perform an action.

[0138] In any several embodiments, the action includes receiving, in one or more processors, the values ​​of each of at least one isotopic feature for a sample from the subject. In any several embodiments, the at least one isotopic feature includes the total concentration of isotopes measured in the sample of the subject, a chemical feature determined from the total concentration of isotopes, the concentration of isotopes of the isotopes, or a chemical feature determined from the concentration of isotopes of the isotopes. In any several embodiments, the at least one isotopic feature includes the total concentration of isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of isotopes of the isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of isotopes of the isotopes measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, an isotope concentration, an isotope concentration of the isotope, or any of the above. In any several embodiments, the at least one isotope feature includes the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject.

[0139] In any several embodiments, the action includes inputting the values ​​of each of the at least one isotopic features of the subject into a predictive model using at least one processor. In any several embodiments, the predictive model is configured to predict the presence or absence of cancer in the subject. In any several embodiments, the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features of a plurality of reference samples. In any several embodiments, the plurality of reference samples originate from a plurality of reference subjects.

[0140] In any and some embodiments, the action includes using a predictive model to determine whether the subject has cancer. In any and some embodiments, the action includes using a predictive model to detect cancer in the subject.

[0141] Furthermore, in some embodiments of this specification, a system for detecting whether a subject has cancer is provided, comprising one or more data processors and a non-temporary computer-readable storage medium containing instructions, wherein, when executed in the one or more data processors, the instructions cause the one or more data processors to (a) receive, in one or more processors, the values ​​of each of at least one isotopic features for a sample from the subject, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample of the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; (b) use at least one processor to input the values ​​of each of the at least one isotopic features of the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features from a plurality of reference samples from a plurality of reference subjects; and (c) use the predictive model to perform an action including determining whether the subject has cancer.

[0142] Furthermore, in some embodiments herein, a system for detecting whether a subject has cancer includes one or more data processors and a non-temporary computer-readable storage medium containing instructions, wherein, when executed in the one or more data processors, the instructions provide to the one or more data processors (a) one or more processors receiving the values ​​of each of at least one isotopic features for a sample from the subject, wherein the values ​​of each of the at least one isotopic features are (1) the concentration of an element, (2) the concentration of an isotope, and (3) the same concentration of the isotope as measured in the sample of the subject. (b) the concentration of the isotope, or a normalized value of any of (4)(1) to (3); (c) using at least one processor to input the values ​​of each of the at least one isotope feature of the subject into a predictive model configured to predict the presence or absence of cancer in the subject, the predictive model comprising at least one machine learning algorithm trained with each of the values ​​of the at least one isotope feature from a plurality of reference samples from a plurality of reference subjects; and (d) using the predictive model to perform an action comprising determining whether the subject has the cancer.

[0143] Furthermore, in some embodiments herein, a system for detecting cancer in a subject includes one or more data processors and a non-temporary computer-readable storage medium containing instructions, wherein, when executed in the one or more data processors, the instructions provide to the one or more data processors (a) one or more processors receiving the values ​​of each of at least one isotopic features for a sample from the subject, wherein the values ​​of each of the at least one isotopic features are (1) the concentration of an element, (2) the concentration of an isotope, and (3) the same concentration of the isotope as measured in the sample of the subject. (4)(1)-(3) is the concentration of the isotope, or a normalized value of any of (1)-(3); (b) using at least one processor to input the values ​​of each of the at least one isotope feature of the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model comprises at least one machine learning algorithm trained with the values ​​of each of the at least one isotope feature from a plurality of reference samples from a plurality of reference subjects; and (c) using the predictive model to perform actions including detecting the cancer in the subject.

[0144] Furthermore, in some embodiments of this specification, a system for assessing the characteristics of cancer in a subject is provided.

[0145] In any several embodiments, the system includes one or more data processors and a non-temporary computer-readable storage medium containing instructions. In any several embodiments, when an instruction is executed in the one or more data processors, it causes the one or more data processors to perform an action.

[0146] In any several embodiments, the action includes receiving, in one or more processors, the values ​​of each of at least one isotopic feature for a sample from the subject. In any several embodiments, the at least one isotopic feature includes the total concentration of isotopes measured in the sample of the subject, a chemical feature determined from the total concentration of isotopes, the concentration of isotopes of the isotopes, or a chemical feature determined from the concentration of isotopes of the isotopes. In any several embodiments, the at least one isotopic feature includes the total concentration of isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of isotopes of the isotopes measured in the sample of the subject. In any several embodiments, the at least one isotopic feature includes the concentration of isotopes of the isotopes measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, an isotope concentration, an isotope concentration of the isotope, or any of the above. In any several embodiments, the at least one isotope feature includes the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the element measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope measured in the sample of the subject.

[0147] In any several embodiments, the action includes inputting the values ​​of each of the at least one isotopic features of the subject into a predictive model using at least one processor. In any several embodiments, the predictive model is configured to predict cancer characteristics in the subject. In any several embodiments, the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features of a plurality of reference samples. In any several embodiments, the plurality of reference samples originate from a plurality of reference subjects.

[0148] In any and all embodiments, the action includes using the predictive model to determine the characteristics of cancer in the subject.

[0149] Furthermore, in some embodiments of this specification, a system for assessing cancer characteristics in a subject is provided, comprising one or more data processors and a non-temporary computer-readable storage medium containing instructions, wherein, when executed in the one or more data processors, the instructions cause the one or more data processors to (a) receive, in one or more processors, the values ​​of each of at least one isotopic features for a sample from the subject, wherein the at least one isotopic feature includes the total concentration of an isotope measured in the sample of the subject, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; (b) use at least one processor to input the values ​​of each of the at least one isotopic features of the subject into a predictive model configured to predict cancer characteristics in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features from a plurality of reference samples from a plurality of reference subjects; and (c) use the predictive model to perform actions including determining cancer characteristics in the subject.

[0150] Furthermore, in some embodiments herein, a system for assessing cancer characteristics in a subject includes one or more data processors and a non-temporary computer-readable storage medium containing instructions, wherein, when executed in the one or more data processors, the instructions provide to the one or more data processors (a) one or more processors receiving the values ​​of each of at least one isotopic features for a sample from the subject, wherein the values ​​of each of the at least one isotopic features are (1) the concentration of an element, (2) the concentration of an isotope, and (3) the isotope, as measured in the sample of the subject. (b) the concentration of the raw isotope, or a normalized value of any of (4)(1) to (3); (c) using at least one processor to input each of the values ​​of the at least one isotopic feature of the subject into a predictive model configured to predict cancer characteristics in the subject, the predictive model comprising at least one machine learning algorithm trained with each of the values ​​of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (d) to cause the predictive model to perform an action comprising determining cancer characteristics in the subject.

[0151] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0152] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis. In some arbitrary embodiments, the value is a normalized value, and determining the value of each of the at least one isotopic feature includes calculating the normalized value from mass spectrometry analysis.

[0153] In some arbitrary embodiments, the mass spectrometry analysis is performed on a liquid sample of the sample. In some arbitrary embodiments, the sample is a solid sample and the mass spectrometry analysis is performed on a liquid sample of the solid sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of the sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of a solid sample.

[0154] In some arbitrary embodiments, the mass spectrometry analysis is performed on a solid sample of the sample. In some arbitrary embodiments, the sample is a solid sample and the mass spectrometry analysis is performed on the solid sample. In some arbitrary embodiments, the solid sample is analyzed via combustion. In some arbitrary embodiments, the sample is a solid sample and the values ​​of each of the at least one isotopic features are obtained by mass spectrometry analysis of the solid sample or a liquid sample of the solid sample.

[0155] In some of the embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample of the sample or a solid sample of the sample. In some of the embodiments, the sample is a solid sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of the solid sample or a liquid sample of the solid sample.

[0156] In some of the embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of the sample or a solid sample analyzed via combustion of the sample. In some of the embodiments, the sample is a solid sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of the solid sample or a liquid sample prepared by digestion of the solid sample. In some of the embodiments, the solid sample is analyzed via combustion.

[0157] In any several embodiments, the mass spectrometry analysis is performed by (i) preparing a liquid sample of the sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and the mass spectrometry analysis is performed by (i) preparing a liquid sample of the sample for analysis by mass spectrometry, the liquid sample being prepared by digestion of the solid sample; (ii) introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared liquid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature.

[0158] In any several embodiments, the mass spectrometry analysis is performed by: (i) preparing a solid sample of the sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared solid sample, wherein the detection includes combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and the mass spectrometry analysis is performed by: (i) preparing the solid sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared solid sample, wherein the detection optionally includes combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature. In any several embodiments, the sample is a solid sample, and the mass spectrometry analysis is performed by (i) preparing the solid sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared solid sample, the detection of which includes combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of the isotopes to create a value for each of the at least one isotopic feature.

[0159] In any several embodiments, the analysis of the detected one or more ionized products of an isotope is accompanied by a reference to one or more ionized products of the element present in a standard material. In any several embodiments, the analysis of the detected one or more ionized products of an element and its isotope is accompanied by a reference to one or more ionized products of the element and its isotope present in a standard material.

[0160] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material comprising multiple calibration standards and blank standards. In any several embodiments, the analysis of the detected one or more ionized products of an isotope and an element involves a reference to one or more ionized products of an element and an isotope present in a standard material comprising multiple calibration standards and blank standards.

[0161] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard and / or a reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including a reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard and a reference material.

[0162] In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and / or a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and a reference material.

[0163] In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of elements present in the standard substance and the prepared liquid or solid sample. In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of elements or isotopes present in the standard substance and the prepared liquid or solid sample.

[0164] In some arbitrary embodiments, the sample comprises keratinous tissue, optionally hard keratinous tissue. In some arbitrary embodiments, the sample comprises keratinous tissue. In some arbitrary embodiments, the sample comprises hard keratinous tissue.

[0165] In some arbitrary aspects, the sample is a non-invasive sample, optionally a hair sample or a nail sample. In some arbitrary aspects, the sample is a non-invasive sample. In some arbitrary aspects, the sample is a hair sample or a nail sample. In some arbitrary aspects, the sample is a hair sample. In some arbitrary aspects, the sample is a nail sample.

[0166] In any given aspect, digestion is by acid digestion and / or thermal digestion. In any given aspect, digestion is by acid digestion and thermal digestion.

[0167] In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3). In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3) and hydrogen peroxide (H2O2).

[0168] In some arbitrary aspects, thermal digestion is performed by microwaves.

[0169] In some arbitrary embodiments, the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS). In some arbitrary embodiments, the ICP-MS is performed using kinetic energy discrimination (KED).

[0170] In any several embodiments, the at least one isotope feature includes atomic percentage, isotope ratio, abundance, delta, total concentration and / or concentration ratio. In any several embodiments, the at least one isotope feature includes atomic percentage. In any several embodiments, the at least one isotope feature includes isotope ratio. In any several embodiments, the at least one isotope feature includes abundance. In any several embodiments, the at least one isotope feature includes delta. In any several embodiments, the at least one isotope feature includes total concentration. In any several embodiments, the at least one isotope feature includes concentration ratio.

[0171] In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the abundance of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the delta of at least one isotope of the isotope.

[0172] In any several embodiments, the at least one isotopic feature is a plurality of isotopic features, each independently selected from the group consisting of atomic percentage, isotopic ratio, abundance, delta, total concentration, and concentration ratio.

[0173] In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the abundance of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the delta of at least one isotope of the isotope.

[0174] In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element, a normalized value of the concentration of an isotope, or a normalized value of the concentration of an isotope of the isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope of the isotope.

[0175] In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%), an abundance (F), an isotope ratio (R), a concentration ratio (CR), or a delta value (delta), or any combination of the aforementioned. In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an abundance (F). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an isotope ratio (R). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a concentration ratio (CR). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a delta value (delta).

[0176] In any several embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In any several embodiments, the delta value is the conventional delta value, the study-specific delta value (InH), the dissimilar simple nitrogen-based delta value (simple Nδ), or the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ). In any several embodiments, the delta value is the conventional delta value. In any several embodiments, the delta value is the study-specific delta value (InH). In any several embodiments, the delta value is the dissimilar simple nitrogen-based delta value (simple Nδ). In any several embodiments, the delta value is the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

[0177] In any given embodiment, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S.

[0178] In some arbitrary embodiments, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In some arbitrary embodiments, the isotope is Ag. In some arbitrary embodiments, the isotope is Al. In some arbitrary embodiments, the isotope is B. In some arbitrary embodiments, the isotope is Ba. In some arbitrary embodiments, the isotope is Br. In some arbitrary embodiments, the isotope is Ca. In some arbitrary embodiments, the isotope is Co. In some arbitrary embodiments, the isotope is Cr. In some arbitrary embodiments, the isotope is Cu. In some arbitrary embodiments, the isotope is Fe. In some arbitrary embodiments, the isotope is Ge. In some arbitrary embodiments, the isotope is Hg. In some arbitrary aspects, the isotope is I. In some arbitrary aspects, the isotope is K. In some arbitrary aspects, the isotope is Li. In some arbitrary aspects, the isotope is Mg. In some arbitrary aspects, the isotope is Mn. In some arbitrary aspects, the isotope is Mo. In some arbitrary aspects, the isotope is Na. In some arbitrary aspects, the isotope is Ni. In some arbitrary aspects, the isotope is Pb. In some arbitrary aspects, the isotope is Rb. In some arbitrary aspects, the isotope is Ru. In some arbitrary aspects, the isotope is Se. In some arbitrary aspects, the isotope is Sn. In some arbitrary aspects, the isotope is Sr. In some arbitrary aspects, the isotope is Te. In some arbitrary aspects, the isotope is Ti. In some arbitrary aspects, the isotope is U. In some arbitrary aspects, the isotope is V. In some arbitrary embodiments, the isotope is Y. In some arbitrary embodiments, the isotope is Zn.

[0179] In some arbitrary aspects, the isotope is N, C, S, or O. In some arbitrary aspects, the isotope is N. In some arbitrary aspects, the isotope is C. In some arbitrary aspects, the isotope is S. In some arbitrary aspects, the isotope is O.

[0180] In some arbitrary embodiments, the isotope is Mo, Cr, Te, C, or Hg. In some arbitrary embodiments, the isotope is Mo. In some arbitrary embodiments, the isotope is Cr. In some arbitrary embodiments, the isotope is Te. In some arbitrary embodiments, the isotope is C. In some arbitrary embodiments, the isotope is Hg.

[0181] In any several embodiments, the at least one isotope feature is one isotope feature. In any several embodiments, the isotope of the at least one isotope feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 Contains Cr. In any several embodiments, the isotope of at least one isotopic feature is 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 68 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 87 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 88 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 86 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 47Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 48 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 46 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 50 Contains Cr. In any several embodiments, the isotope of at least one isotopic feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and 50 Contains Cr.

[0182] In some arbitrary embodiments, the at least one isotope feature is a plurality of isotope features. In some arbitrary embodiments, the plurality of isotope features is about 2 to about 100 isotope features. In some arbitrary embodiments, the plurality of isotope features is 2 to 100 isotope features. In any several embodiment, the plurality of isotopic features include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 or more isotopic features. In any several embodiment, the plurality of isotopic features include at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopic features.

[0183] In any and all embodiments, the at least one isotopic feature is a plurality of isotopic features determined from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or more elements or isotopes. In any several embodiment, the plurality of isotopic features are determined from at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes.

[0184] In any several embodiments, the plurality of isotopic features include abundance and delta. In any several embodiments, abundance and delta values ​​are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of the isotope.

[0185] In any several embodiments, the plurality of isotopic features include abundance, atomic percentage, and / or isotopic ratio. In any several embodiments, abundance, atomic percentage, and / or isotopic ratios are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of an isotope.

[0186] In any several embodiments, the isotopes include Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, and / or S. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Na, Mg, Al, P, K, Sc, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, I, Te, Ba, Hg, Pb, U, C, N, O, or S, or any combination of the aforementioned. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S, or any combination of the aforementioned. In any several embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Mg, Ca, Ti, Cr, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Mo, Ag, Sn, Ba, Hg, Pb, U, C, N, S, or any combination of the foregoing.

[0187] In any and some embodiments, the isotope contains Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In any and some embodiments, the isotope contains Ag. In any and some embodiments, the isotope contains Al. In any and some embodiments, the isotope contains B. In any and some embodiments, the isotope contains Ba. In any and some embodiments, the isotope contains Br. In any and some embodiments, the isotope contains Ca. In any and some embodiments, the isotope contains Co. In any and some embodiments, the isotope contains Cr. In any and some embodiments, the isotope contains Cu. In any and some embodiments, the isotope contains Fe. In any and some embodiments, the isotope contains Ge. In any and some embodiments, the isotope contains Hg. In some arbitrary embodiments, the isotope contains I. In some arbitrary embodiments, the isotope contains K. In some arbitrary embodiments, the isotope contains Li. In some arbitrary embodiments, the isotope contains Mg. In some arbitrary embodiments, the isotope contains Mn. In some arbitrary embodiments, the isotope contains Mo. In some arbitrary embodiments, the isotope contains Na. In some arbitrary embodiments, the isotope contains Ni. In some arbitrary embodiments, the isotope contains Pb. In some arbitrary embodiments, the isotope contains Rb. In some arbitrary embodiments, the isotope contains Ru. In some arbitrary embodiments, the isotope contains Se. In some arbitrary embodiments, the isotope contains Sn. In some arbitrary embodiments, the isotope contains Sr. In some arbitrary embodiments, the isotope contains Te. In some arbitrary embodiments, the isotope contains Ti. In some arbitrary embodiments, the isotope contains U. In some arbitrary embodiments, the isotope contains V. In some arbitrary embodiments, the isotope contains Y. In some arbitrary embodiments, the isotope contains Zn. In some arbitrary embodiments, the isotopes contain Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, and Zn.

[0188] In some arbitrary embodiments, the isotope contains N, C, S, and / or O. In some arbitrary embodiments, the isotope contains N. In some arbitrary embodiments, the isotope contains C. In some arbitrary embodiments, the isotope contains S. In some arbitrary embodiments, the isotope contains O. In some arbitrary embodiments, the isotope contains N, C, S, and O.

[0189] In any and some embodiments, the isotopes include Mo, Cr, Te, C and / or Hg. In any and some embodiments, the isotopes include Mo. In any and some embodiments, the isotopes include Cr. In any and some embodiments, the isotopes include Te. In any and some embodiments, the isotopes include C. In any and some embodiments, the isotopes include Hg. In any and some embodiments, the isotopes include Mo, Cr, Te, C and Hg.

[0190] In some arbitrary embodiments, at least one isotope feature includes a concentration ratio. In some arbitrary embodiments, the concentration ratio is N / C, S / C, O / C, S / N, N / O, or S / O. In some arbitrary embodiments, the concentration ratio is N / C. In some arbitrary embodiments, the concentration ratio is S / C. In some arbitrary embodiments, the concentration ratio is O / C. In some arbitrary embodiments, the concentration ratio is S / N. In some arbitrary embodiments, the concentration ratio is N / O. In some arbitrary embodiments, the concentration ratio is S / O.

[0191] In any several embodiments, the plurality of isotope features include at least one concentration ratio. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and / or S / O. In any several embodiments, the at least one concentration ratio includes N / C. In any several embodiments, the at least one concentration ratio includes S / C. In any several embodiments, the at least one concentration ratio includes O / C. In any several embodiments, the at least one concentration ratio includes S / N. In any several embodiments, the at least one concentration ratio includes N / O. In any several embodiments, the at least one concentration ratio includes S / O. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and S / O.

[0192] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (f34S), relative to stable isotopes of N 15 The present invention includes at least one, two, three, four, five, six, seven, eight, nine, or ten isotopic features from among the abundance, atomic percentage, or isotopic ratio of N (f15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO). In any several embodiments, the at least one isotopic feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (f34S), relative to stable isotopes of N15 This includes the abundance of N, atomic percentage or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0193] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio (F13C) of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of O. 18 The abundance of O includes the ratio, atomic percentage, or isotopic ratio (F18O). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to nitrogen (SdN). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio (F34S) of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of N. 15 This includes the abundance, atomic percentage, or isotopic ratio of N (F15N). In any several embodiments, the at least one isotopic feature includes the concentration ratio of oxygen to carbon (OdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to oxygen (NdO). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to carbon (SdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to oxygen (SdO).

[0194] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (F34S), relative to stable isotopes of N 15 This includes the abundance of N, atomic percentage or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0195] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn, 67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and 98Each of the isotopes of Mo contains at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more isotopic ratios, atomic percentages, or isotopic ratios of the stable isotopes of that isotope.

[0196] In any several embodiments, the at least one isotope feature is relative to a stable isotope of Ru. 101 This includes the abundance, atomic percentage, or isotopic ratio of Ru. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 107 The abundance of Ag includes the relative abundance, atomic percentage, or isotopic ratio. In any several embodiments, the at least one isotopic feature includes the relative abundance, atomic percentage, or isotopic ratio of 10⁹Ag to the stable isotope of Ag. In any several embodiments, the at least one isotopic feature includes the relative abundance of B to the stable isotope of B 10 The abundance, atomic percentage, or isotopic ratio of B is included. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Sn. 118 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Sn. 119 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Sn. 124 This includes the abundance, atomic percentage, or isotopic ratio of Sn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Te. 124 This includes the abundance, atomic percentage, or isotopic ratio of Te. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 The abundance of I includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Te. 128 This includes the abundance, atomic percentage, or isotopic ratio of Te. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ba. 136This includes the abundance, atomic percentage, or isotopic ratio of Ba. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ba. 137 This includes the abundance, atomic percentage, or isotopic ratio of Ba. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 235 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 238 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Na. 23 This includes the abundance, atomic percentage, or isotopic ratio of Na. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Al. 27 This includes the abundance, atomic percentage, or isotopic ratio of Al. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of K. 39 This includes the abundance, atomic percentage, or isotopic ratio of K. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ti. 48 This includes the abundance, atomic percentage, or isotopic ratio of Ti. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Cr. 54 This includes the abundance, atomic percentage, or isotopic ratio of Cr. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 54This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In some embodiments, the at least one isotopic feature is relative to the stable isotope of Ni. 62 This includes the abundance, atomic percentage, or isotopic ratio of Ni. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Cu. 63 This includes the abundance, atomic percentage, or isotopic ratio of Cu. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 66 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 The abundance, atomic percentage, or isotopic ratio of Zn is included. In any several embodiments, the at least one isotopic feature includes the abundance, atomic percentage, or isotopic ratio of 68Zn relative to the stable isotope of Zn. In any several embodiments, the at least one isotopic feature includes relative to the stable isotope of Ge 70 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ge. 74 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 76 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 77 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Br. 79This includes the abundance, atomic percentage, or isotopic ratio of Br. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 80 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 82 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Y. 89 The abundance, atomic percentage, or isotopic ratio of Y is included. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 98 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0197] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn,67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and 98 This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0198] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al, 34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 Each of the isotopes of Mo contains at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more isotopic ratios, atomic percentages, or isotopic ratios of the isotope relative to its stable isotope.

[0199] In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 107 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 109 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Sn. 118 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127This includes the abundance, atomic percentage, or isotopic ratio of I. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 235 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Al. 27 This includes the abundance, atomic percentage, or isotopic ratio of Al. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0200] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al,34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0201] In any several embodiments, the at least one isotope feature is relative to the stable isotope of I. 127 The abundance of I includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 Abundance of I, atomic percentage or isotopic ratio, and relative to stable isotopes of Ca 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca.

[0202] In any several embodiments, the isotope of the at least one isotope feature is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn,77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 Includes U, or any combination of the aforementioned.

[0203] In any and some embodiments, the isotope of the at least one isotope feature contains Zn. In any and some embodiments, the isotope of the at least one isotope feature is 64 Zn or 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Contains Zn. The isotope of at least one isotopic feature is 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Zn and 66 Contains Zn.

[0204] In any several embodiments, the isotope of the at least one isotope feature contains S. In any several embodiments, the isotope of the at least one isotope feature is 34 Includes S.

[0205] In any and some embodiments, the isotope of the at least one isotope feature includes N, C, S, or O. In any and some embodiments, the isotope of the at least one isotope feature includes N. In any and some embodiments, the isotope of the at least one isotope feature includes C. In any and some embodiments, the isotope of the at least one isotope feature includes S. In any and some embodiments, the isotope of the at least one isotope feature includes O. In any and some embodiments, the isotopes of the at least one isotope feature include N, C, S, and O.

[0206] In some arbitrary manner, a machine learning algorithm is a supervised machine learning algorithm.

[0207] In some arbitrary aspects, the predictive model is a classification model. In some arbitrary aspects, the predictive model is a binary classification model. In some arbitrary aspects, the predictive model is a multiclass classification model.

[0208] In some arbitrary aspects, the machine learning algorithm is an ensemble algorithm that includes logistic regression (e.g., ridge, lasso, or elastic net), naive Bayes classifier, Gaussian NB, Gaussian NB isotonic, Gaussian NB sigmoid, random forest classifier, gradient boosting classifier, XG boost classifier, histogram gradient boosting classifier, deep neural network classifier, decision tree classifier, bagging classifier, extra tree classifier, Ada Boost classifier, Cat Boost classifier, LGBM classifier, support vector classifier, perceptron classifier (e.g., multilayer perceptron classifier), deep learning classifier for tabular data (e.g., TabNet or TabML), passive-aggressive classifier or SGD classifier, or any combination of the aforementioned.

[0209] In some arbitrary aspects, the machine learning algorithm is a support vector classifier, a decision tree-based ensemble classifier, or a multilayer perceptron classifier.

[0210] In some arbitrary manner, the predictive model is a regression model.

[0211] In some arbitrary manner, the machine learning algorithm is an unsupervised machine learning algorithm.

[0212] In some arbitrary embodiments, the predictive model is a general model. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, and / or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area compared to another of the multiple reference subjects. In some arbitrary embodiments, the multiple reference subjects originate from at least two, three, four, five, six or more different countries. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different sex compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different age compared to another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects belongs to a different cultural or ethnic group than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different dietary habit than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different type of cancer than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different dietary habit, and has a different type of cancer than another of the multiple reference subjects.

[0213] In some arbitrary aspects, the predictive model is a specialized model. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and / or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area. In some arbitrary aspects, each of the multiple reference subjects is of the same sex. In some arbitrary aspects, each of the multiple reference subjects is male. In some arbitrary aspects, each of the multiple reference subjects is female. In some arbitrary aspects, each of the multiple reference subjects is of the same age. In some arbitrary aspects, each of the multiple reference subjects has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and has the same type of cancer.

[0214] In some arbitrary aspects, the subject is a mammal. In some arbitrary aspects, the subject is a human.

[0215] In any and some aspects, the cancer is a blood cancer or a solid tumor. In any and some aspects, the cancer is a blood cancer. In any and some aspects, the blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some aspects, the blood cancer is lymphoma. In any and some aspects, the blood cancer is multiple myeloma. In any and some aspects, the blood cancer is leukemia. In any and some aspects, the cancer is a solid tumor.

[0216] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0217] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0218] In any several embodiments, the predictive model is constructed by (i) determining the value of each of the at least one isotopic feature for the plurality of reference samples; and (ii) training a machine learning algorithm using the values ​​of each of the at least one isotopic feature for the plurality of reference samples. In any several embodiments, the predictive model is trained by (i) determining the value of each of the at least one isotopic feature for the plurality of reference samples; and (ii) training a machine learning algorithm using the values ​​of each of the at least one isotopic feature for the plurality of reference samples.

[0219] In some arbitrary aspects, some of the reference objects are known to have cancer. In some arbitrary aspects, it is assumed that some of the reference objects do not have cancer. In some arbitrary aspects, some of the reference objects are known to have cancer, and it is assumed that some of the reference objects do not have cancer.

[0220] In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples. In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples.

[0221] In some arbitrary embodiments, the label indicates whether the reference sample originates from a reference subject known to have cancer or from a reference subject assumed not to have cancer.

[0222] In some arbitrary aspects, the label indicates the characteristics of the reference cancer from which the acquired reference sample originates.

[0223] In some arbitrary embodiments, the label indicates the tumor burden level of the reference cancer from which the acquired reference sample originates.

[0224] In any several embodiments, the method further includes a step of preprocessing each of the values ​​of the at least one isotope feature prior to the step of training a machine learning algorithm. In any several embodiments, the preprocessing step includes comparative comparison imputation of missing values. In any several embodiments, the preprocessing step includes replacing missing values ​​with zeros. In any several embodiments, the preprocessing step includes normalizing the data.

[0225] In some arbitrary aspects, cancer is detected in a subject if the predictive model indicates the presence of cancer.

[0226] Furthermore, in some embodiments of this specification, a method for constructing a predictive model for detecting whether a subject has cancer is provided. Furthermore, in some embodiments of this specification, a method for training a predictive model for detecting whether a subject has cancer is provided. In any several embodiments, the method includes the step of determining the value of each of at least one isotopic feature for a plurality of reference samples. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of the isotope, the concentration of the isotope, or a chemical feature determined from the concentration of the isotope. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the isotope of the isotope measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the isotope of the isotope measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the element, the concentration of the isotope, the concentration of the isotope of the isotope, or a normalized value of any of these, measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the element measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the isotope measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes the concentration of the isotope of the isotope measured in one of the multiple reference samples.In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the element measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the isotope measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of the isotope of the isotope measured in one of the multiple reference samples.

[0227] In some arbitrary embodiments, the multiple reference samples are derived from multiple reference objects.

[0228] In any several embodiments, the method includes constructing a predictive model. In any several embodiments, the method includes training the predictive model. In any several embodiments, the predictive model is configured to predict the presence or absence of cancer in a subject. In any several embodiments, the predictive model includes at least one machine learning algorithm. In any several embodiments, the construction includes training the machine learning algorithm using the values ​​of each of the at least one isotopic features for the plurality of reference samples. In any several embodiments, training the machine learning algorithm includes using the values ​​of each of the at least one isotopic features for the plurality of reference samples. For example, the method involves applying the values ​​of each of the at least one isotopic features for the plurality of reference samples as input for training the machine learning algorithm.

[0229] Furthermore, in some embodiments of this specification, a method is provided for constructing a predictive model for detecting whether a subject has cancer, comprising the steps of: (a) determining the value of each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein the at least one isotopic feature includes the total concentration of an isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; and (b) constructing a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm, and the construction comprises training the machine learning algorithm with the values ​​of each of the at least one isotopic feature for the plurality of reference samples.

[0230] Furthermore, in some embodiments of this specification, a method is provided for training a predictive model to detect whether a subject has cancer, comprising the steps of: (a) determining the value of each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein the value of each of the at least one isotopic feature is a normalized value of any of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the reference samples among the plurality of reference subjects; and (b) training a predictive model configured to predict whether a subject has cancer, wherein the predictive model comprises at least one machine learning algorithm.

[0231] Furthermore, in some embodiments of this specification, methods for constructing predictive models for assessing the characteristics of cancer in a subject are provided. Furthermore, in some embodiments of this specification, methods for training predictive models for assessing the characteristics of cancer in a subject are provided.

[0232] In any several embodiments, the method includes a step of determining the value of each of at least one isotopic feature for a plurality of reference samples. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of the isotope, the concentration of the isotope, or a chemical feature determined from the concentration of the isotope. In any several embodiments, the at least one isotopic feature includes the total concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotopic feature includes a chemical feature determined from the total concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in one of the plurality of reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, an isotope, an isotope, or any of the above, as measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, an isotope, an isotope, or any of the above, as measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an element, as measured in one of the multiple reference samples. In any several embodiments, the at least one isotope feature includes a normalized value of the concentration of an isotope, as measured in one of the multiple reference samples.In any and all embodiments, the at least one isotope feature includes a normalized value of the concentration of the isotope of the isotope element measured in one of the plurality of reference samples.

[0233] In some arbitrary embodiments, the multiple reference samples are derived from multiple reference objects.

[0234] In any several embodiments, the method includes constructing a predictive model. In any several embodiments, the method includes training the predictive model. In any several embodiments, the predictive model is configured to predict cancer characteristics in a subject. In any several embodiments, the predictive model includes at least one machine learning algorithm. In any several embodiments, the construction includes training the machine learning algorithm using the values ​​of each of the at least one isotopic features for the plurality of reference samples. In any several embodiments, the training of the machine learning algorithm includes using the values ​​of each of the at least one isotopic features for the plurality of reference samples.

[0235] Furthermore, in some embodiments herein, a method is provided for constructing a predictive model for assessing cancer characteristics in a subject, comprising the steps of: (a) determining the value of each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein the at least one isotopic feature includes the total concentration of an isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope of an isotope, or a chemical feature determined from the concentration of an isotope of an isotope; and (b) constructing a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm, and the construction includes training the machine learning algorithm with the values ​​of each of the at least one isotopic feature for the plurality of reference samples.

[0236] Furthermore, in some embodiments of this specification, a method for training a predictive model for assessing cancer characteristics in a subject is provided, comprising the steps of: (a) determining the value of each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3) measured in one of the reference samples; and (b) applying the value of each of the at least one isotopic feature for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model comprises at least one machine learning algorithm.

[0237] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0238] In any several embodiments, determining the value of each of the at least one isotopic features includes mass spectrometry analysis. In any several embodiments, the value is a normalized value, and determining the value of each of the at least one isotopic features includes calculating the normalized value from mass spectrometry analysis.

[0239] In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples from the solid reference sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing one or more liquid samples from the reference sample for analysis by mass spectrometry. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes preparing one or more liquid samples from the solid reference sample for analysis by mass spectrometry. In some arbitrary embodiments, the liquid sample is prepared by digesting one or more of the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and the liquid sample is prepared by digesting one or more of the solid reference sample.

[0240] In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid samples from the solid reference sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes preparing one or more solid samples from the reference sample for analysis by mass spectrometry. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes preparing one or more solid reference samples for analysis by mass spectrometry. In some arbitrary embodiments, the solid sample is analyzed via combustion. In some arbitrary embodiments, the solid reference sample is analyzed via combustion.

[0241] In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples and / or one or more solid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid reference samples and / or one or more liquid samples from the solid reference sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples from the reference sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples from the reference sample. In some arbitrary embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid samples from the reference sample. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more of the solid reference samples. In any several embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples and one or more solid samples of the reference samples. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more of the solid reference samples and one or more liquid samples of the solid reference samples.

[0242] In any several embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid samples of the reference samples, which are analyzed by digestion of one or more of the reference samples and / or by combustion. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of the solid reference sample, which is optionally analyzed by combustion and / or by a liquid sample prepared by digestion of one or more of the solid reference samples. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more solid reference samples, which are analyzed by combustion and / or by a liquid sample prepared by digestion of one or more of the solid reference samples. In some arbitrary embodiments, determining the value of each of the at least one isotopic features includes mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the reference samples. In some arbitrary embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic features includes mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the solid reference samples. In some arbitrary embodiments, determining the value of each of the at least one isotopic features includes mass spectrometry analysis of one or more of the solid samples analyzed via combustion. In some arbitrary embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic features includes mass spectrometry analysis of one or more of the solid reference samples, which may be analyzed via combustion. In some arbitrary embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic features includes mass spectrometry analysis of one or more of the solid reference samples analyzed via combustion.In any several embodiments, determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of a liquid sample prepared by the digestion of one or more of the reference samples and one or more solid samples of the reference samples analyzed via combustion. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more of the solid reference samples and a liquid sample prepared by the digestion of one or more of the solid reference samples, which may be analyzed via combustion. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more of the solid reference samples and a liquid sample prepared by the digestion of one or more of the solid reference samples, which may be analyzed via combustion.

[0243] In any several embodiments, determining the value of each of the at least one isotopic feature includes (i) preparing one or more liquid samples of the reference sample for analysis by mass spectrometry, the liquid samples being prepared by digestion of the one or more reference samples; (ii) introducing the prepared liquid samples into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared liquid samples; and (iii) analyzing the detected one or more ionized products of the isotopes to create one or more values ​​of the at least one isotopic feature for the prepared liquid samples. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes (i) preparing one or more liquid samples of the solid reference sample for analysis by mass spectrometry, the liquid samples being prepared by digestion of the one or more solid reference samples; (ii) introducing the prepared liquid samples into a mass spectrometer to detect isotopes and one or more ionized products of elements present in the prepared liquid samples; and (iii) analyzing the detected one or more ionized products of isotopes and elements to create one or more values ​​of the at least one isotopic feature for the prepared liquid samples.

[0244] In any several embodiments, determining the value of each of the at least one isotopic feature comprises (i) preparing one or more solid samples of the reference sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared solid sample, the detection of which includes combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes to create one or more values ​​of the at least one isotopic feature for the prepared solid sample. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes (i) preparing one or more of the solid reference samples for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes and elements present in the prepared solid sample, optionally including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes and elements to create one or more values ​​of the at least one isotopic feature for the prepared solid sample. In any several embodiments, the reference sample is a solid sample, and determining the value of each of the at least one isotopic feature includes (i) preparing one or more of the solid reference samples for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes and elements present in the prepared solid sample, the detection including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes and elements to create one or more values ​​of the at least one isotopic feature for the prepared solid sample.

[0245] In some arbitrary aspects, the decision and / or construction is performed by the processor of the computing device. In some arbitrary aspects, the decision and / or training is performed by the processor of the computing device. In some arbitrary aspects, the decision is performed by the processor of the computing device. In some arbitrary aspects, the construction is performed by the processor of the computing device. In some arbitrary aspects, the training is performed by the processor of the computing device. In some arbitrary aspects, the decision and construction are performed by the processor of the computing device. In some arbitrary aspects, the decision and training are performed by the processor of the computing device.

[0246] Furthermore, in some embodiments herein, a method is provided for constructing a predictive model for detecting whether a subject has cancer, comprising: (a) using one or more processors to receive values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein the at least one isotopic feature includes the total concentration of an isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; and (b) using at least one processor to construct a predictive model configured to predict whether a subject has cancer, wherein the predictive model includes at least one machine learning algorithm, and the construction includes training the machine learning algorithm with the values ​​for each of the at least one isotopic feature for the plurality of reference samples.

[0247] Furthermore, in some embodiments of this specification, a method is provided for training a predictive model to detect whether a subject has cancer, comprising: (a) using one or more processors to receive values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein each value of the at least one isotopic feature is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the reference samples among the plurality of reference subjects; and (b) applying each value of the at least one isotopic feature for the plurality of reference samples as input for training a predictive model configured to predict the presence or absence of cancer in a subject using at least one processor, wherein the predictive model comprises at least one machine learning algorithm.

[0248] Furthermore, in some embodiments herein, a method is provided for constructing a predictive model for assessing cancer characteristics in a subject, comprising: (a) using one or more processors to receive values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein the at least one isotopic feature includes the total concentration of an isotope measured in one of the plurality of reference samples, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope, or a chemical feature determined from the concentration of an isotope; and (b) using at least one processor to construct a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm, and the construction includes training the machine learning algorithm with the values ​​for each of the at least one isotopic feature for the plurality of reference samples.

[0249] Furthermore, in some embodiments herein, a method is provided for training a predictive model for assessing cancer characteristics in a subject, comprising: (a) using one or more processors to receive values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference subjects, wherein each value of the at least one isotopic feature is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the reference samples among the plurality of reference samples; and (b) applying each value of the at least one isotopic feature for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject using at least one processor, wherein the predictive model comprises at least one machine learning algorithm.

[0250] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0251] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis. In some arbitrary embodiments, the value is a normalized value, and determining the value of each of the at least one isotopic feature includes calculating the normalized value from mass spectrometry analysis.

[0252] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more liquid samples from the solid reference sample. In some arbitrary embodiments, the liquid sample is prepared by digestion of one or more of the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and the liquid sample is prepared by digestion of one or more of the solid reference sample.

[0253] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more solid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of one or more of the solid reference samples. In some arbitrary embodiments, the reference sample is a solid reference sample, and determining the value of each of the at least one isotopic feature includes preparing one or more of the solid reference samples for analysis by mass spectrometry. In some arbitrary embodiments, the solid sample is analyzed via combustion. In some arbitrary embodiments, the solid reference sample is analyzed via combustion.

[0254] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples and / or one or more solid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples from the solid reference sample. In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples from the solid reference sample. In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more solid samples from the reference sample. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples. In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more liquid samples and / or solid samples of the reference samples. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples and one or more liquid samples of the solid reference samples.

[0255] In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the reference samples and / or a solid sample analyzed by combustion of one or more of the reference samples. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the solid reference samples and / or a solid reference sample, which may be analyzed by combustion. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the solid reference samples and / or a solid reference sample, which may be analyzed by combustion. In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the reference samples. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the reference samples. In some arbitrary embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more solid samples of the reference sample, which are analyzed via combustion. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples, which may be analyzed via combustion. In some arbitrary embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples, which are analyzed via combustion.In any several embodiments, the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digesting one or more of the reference samples and a liquid sample prepared by digesting one or more of the reference samples, which is analyzed via combustion. In any several embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples and a liquid sample prepared by digesting one or more of the solid reference samples, which may be analyzed via combustion. In any several embodiments, the reference sample is a solid reference sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of one or more of the solid reference samples and a liquid sample prepared by digesting one or more of the solid reference samples, which is analyzed via combustion.

[0256] In any several embodiments, prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing one or more liquid samples of the reference samples for analysis by mass spectrometry, the liquid samples being prepared by digestion of the one or more reference samples; (ii) introducing the prepared liquid samples into a mass spectrometer to detect one or more ionized products of the isotopes present in the prepared liquid samples; and (iii) analyzing the detected one or more ionized products of the isotopes to create one or more values ​​of the at least one isotopic feature for the prepared liquid samples. In any several embodiments, the reference sample is a solid reference sample, and the method includes, prior to step (a), a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing one or more liquid samples of the solid reference sample for analysis by mass spectrometry, the liquid samples being prepared by digestion of the one or more reference samples; (ii) introducing the prepared liquid samples into a mass spectrometer to detect isotopes and one or more ionized products of elements present in the prepared liquid samples; and (iii) analyzing the detected one or more ionized products of isotopes and elements to create one or more values ​​of the at least one isotopic feature for the prepared liquid samples.

[0257] In any several embodiments, prior to step (a), the method includes a step of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing one or more solid samples of the reference sample for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of isotopes present in the prepared solid sample, the detection including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes to create one or more values ​​of the at least one isotopic feature for the prepared solid sample. In any several embodiments, the reference sample is a solid reference sample, and the method includes a step prior to step (a) of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing one or more of the solid reference samples for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect isotopes and one or more ionized products of elements present in the prepared solid sample, optionally including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes to create one or more values ​​of the at least one isotopic feature for the prepared solid sample.In any several embodiments, the reference sample is a solid reference sample, and the method includes a step prior to step (a) of determining the value of each of the at least one isotopic feature, wherein the determining step includes (i) preparing one or more of the solid reference samples for analysis by mass spectrometry; (ii) introducing the prepared solid sample into a mass spectrometer to detect isotopes and one or more ionized products of elements present in the prepared solid sample, the detection including combustion of the prepared solid sample; and (iii) analyzing the detected one or more ionized products of isotopes to create one or more values ​​of the at least one isotopic feature for the prepared solid sample.

[0258] In any several embodiments, the analysis of the detected one or more ionized products of an isotope is accompanied by a reference to one or more ionized products of the element present in a standard material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope and an element is accompanied by a reference to one or more ionized products of the element and an isotope present in a standard material.

[0259] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material comprising multiple calibration standards and blank standards. In any several embodiments, the analysis of the detected one or more ionized products of an isotope and an element involves a reference to one or more ionized products of an element and an isotope present in a standard material comprising multiple calibration standards and blank standards.

[0260] In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard and / or reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope and an element involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and / or reference material. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including an internal standard. In any several embodiments, the analysis of the detected one or more ionized products of an isotope and an element involves a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard. In any several embodiments, the analysis of the detected one or more ionized products of an isotope involves a reference to one or more ionized products of an element present in a standard material including a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected is accompanied by a reference to one or more ionized products of the element and its isotope present in a standard material containing a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope that has been detected is accompanied by a reference to one or more ionized products of the element present in a standard material containing an internal standard material and a reference material. In any several embodiments, the analysis of one or more ionized products of an isotope and an element that has been detected is accompanied by a reference to one or more ionized products of the element and its isotope present in a standard material containing an internal standard material and a reference material.

[0261] In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of the elements present in the standard substance and the prepared liquid or solid sample. In any several embodiments, the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of the elements and isotopes present in the standard substance and the prepared liquid or solid sample.

[0262] Furthermore, in some embodiments herein, (a) preparing multiple reference samples obtained from multiple reference objects; (b) preparing liquid and / or solid samples from the multiple reference samples for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of one or more of the reference samples; (c) preparing a standard material comprising multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionized products of elements present in the standard material and in the prepared liquid or solid sample, respectively; (e) determining the value of each of at least one isotopic feature for the multiple reference samples, by determining the detected one or more isotopic features of the isotopes. The present invention provides a method for constructing a predictive model for detecting whether a subject has cancer, comprising: (f) analyzing an onion product, wherein the at least one isotopic feature includes the total concentration of an isotope, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope of an isotope, or a chemical feature determined from the concentration of an isotope of an isotope; (g) receiving the respective values ​​of the at least one isotopic feature in one or more processors; and (g) constructing a predictive model configured to predict the presence or absence of cancer in the subject using the at least one processor, wherein the predictive model includes at least one machine learning algorithm, and the construction comprises training the machine learning algorithm with the respective values ​​of the at least one isotopic feature for the plurality of reference samples.

[0263] Furthermore, in some embodiments herein, (a) preparing multiple solid reference samples obtained from multiple reference objects; (b) preparing liquid and / or solid samples from the multiple solid reference samples for analysis by mass spectrometry, wherein the liquid samples are prepared by digestion of one or more of the solid reference samples; (c) preparing a standard material comprising multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) creating values ​​for each of at least one isotopic features for the multiple reference samples. The present invention provides a method for training a predictive model to detect whether a subject has cancer, comprising: (f) analyzing the isotopic elements and one or more ionized products of the elements detected, wherein the value of each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) receiving the value of each of the at least one isotopic feature in one or more processors; and (g) applying the value of each of the at least one isotopic feature for the plurality of reference samples as input for training a predictive model configured to predict the presence or absence of cancer in the subject using at least one processor, wherein the predictive model comprises at least one machine learning algorithm.

[0264] Furthermore, in some embodiments herein, (a) preparing multiple reference samples obtained from multiple reference objects; (b) preparing liquid and / or solid samples from the multiple reference samples for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of one or more of the reference samples; (c) preparing a standard material comprising multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionization products of elements present in the standard material and in each of the prepared liquid or solid sample; (e) detecting one or more ionization products of the isotopes of each of the multiple reference samples to create a value for each of at least one isotopic feature for the multiple reference samples. The present invention provides a method for constructing a predictive model for assessing cancer characteristics in a subject, comprising: (f) analyzing a ionization product wherein the at least one isotopic feature includes the total concentration of an isotope, a chemical feature determined from the total concentration of an isotope, the concentration of an isotope of an isotope, or a chemical feature determined from the concentration of an isotope of an isotope; (g) receiving the respective values ​​of the at least one isotopic feature in one or more processors; and (g) constructing a predictive model configured to predict cancer characteristics in a subject using the at least one processor, wherein the predictive model includes at least one machine learning algorithm, and the construction comprises training the machine learning algorithm with the respective values ​​of the at least one isotopic feature for the plurality of reference samples.

[0265] Furthermore, in some embodiments herein, (a) preparing multiple solid reference samples obtained from multiple reference objects; (b) preparing liquid and / or solid samples from the multiple solid reference samples for analysis by mass spectrometry, wherein the liquid samples are prepared by digestion of one or more of the solid reference samples; (c) preparing a standard material comprising multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) creating values ​​for each of at least one isotopic features for the multiple reference samples, The present invention provides a method for training a predictive model for assessing cancer characteristics in a subject, comprising: (f) analyzing one or more ionized products of the isotopes, wherein the value of each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) receiving the value of each of the at least one isotopic feature in one or more processors; and (g) applying the value of each of the at least one isotopic feature for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject using at least one processor, wherein the predictive model comprises at least one machine learning algorithm.

[0266] In some arbitrary aspects, the characteristic of cancer is the tumor burden level of cancer in the subject.

[0267] In any several embodiments, each of the multiple reference samples includes keratinous tissue, optionally hard keratinous tissue. In any several embodiments, each of the multiple reference samples includes keratinous tissue. In any several embodiments, each of the multiple reference samples includes hard keratinous tissue.

[0268] In any several embodiments, the multiple reference samples are non-invasive samples, optionally hair samples and / or nail samples. In any several embodiments, the multiple reference samples are non-invasive samples. In any several embodiments, the multiple reference samples are hair samples and / or nail samples. In any several embodiments, the multiple reference samples are hair samples. In any several embodiments, the multiple reference samples are nail samples. In any several embodiments, the multiple reference samples are hair samples and nail samples.

[0269] In any given aspect, digestion is by acid digestion and / or thermal digestion. In any given aspect, digestion is by acid digestion and thermal digestion.

[0270] In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3). In some arbitrary embodiments, the acid digestion uses nitric acid (HNO3) and hydrogen peroxide (H2O2).

[0271] In some arbitrary aspects, thermal digestion is performed by microwaves.

[0272] In some arbitrary embodiments, the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS). In some arbitrary embodiments, the ICP-MS is performed using kinetic energy discrimination (KED).

[0273] In any several embodiments, the at least one isotope feature includes atomic percentage, isotope ratio, abundance, delta, total concentration and / or concentration ratio. In any several embodiments, the at least one isotope feature includes atomic percentage. In any several embodiments, the at least one isotope feature includes isotope ratio. In any several embodiments, the at least one isotope feature includes abundance. In any several embodiments, the at least one isotope feature includes delta. In any several embodiments, the at least one isotope feature includes total concentration. In any several embodiments, the at least one isotope feature includes concentration ratio.

[0274] In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the atomic percentage of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the abundance of at least one isotope of the isotope. In any several embodiments, the at least one isotope feature includes the delta of at least one isotope of the isotope.

[0275] In any several embodiments, the at least one isotopic feature is a plurality of isotopic features, each independently selected from the group consisting of atomic percentage, isotopic ratio, abundance, delta, total concentration, and concentration ratio.

[0276] In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope, the isotopic ratio of at least one isotope of the isotope, the abundance of at least one isotope of the isotope, and / or the delta of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the atomic percentage of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the isotopic ratio of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the abundance of at least one isotope of the isotope. In any several embodiments, the plurality of isotope features include the delta of at least one isotope of the isotope.

[0277] In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element, a normalized value of the concentration of an isotope, or a normalized value of the concentration of an isotope of the isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an element. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope. In any and some embodiments, each value of the at least one isotope feature is a normalized value of the concentration of an isotope of the isotope.

[0278] In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%), an abundance (F), an isotope ratio (R), a concentration ratio (CR), or a delta value (delta), or any combination of the aforementioned. In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an atomic percentage (atomic%). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an abundance (F). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is an isotope ratio (R). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a concentration ratio (CR). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is a delta value (delta).

[0279] In any several embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In any several embodiments, the delta value is the conventional delta value, the study-specific delta value (InH), the dissimilar simple nitrogen-based delta value (simple Nδ), or the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ). In any several embodiments, the delta value is the conventional delta value. In any several embodiments, the delta value is the study-specific delta value (InH). In any several embodiments, the delta value is the dissimilar simple nitrogen-based delta value (simple Nδ). In any several embodiments, the delta value is the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

[0280] In any given embodiment, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S.

[0281] In some arbitrary embodiments, the isotope is Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In some arbitrary embodiments, the isotope is Ag. In some arbitrary embodiments, the isotope is Al. In some arbitrary embodiments, the isotope is B. In some arbitrary embodiments, the isotope is Ba. In some arbitrary embodiments, the isotope is Br. In some arbitrary embodiments, the isotope is Ca. In some arbitrary embodiments, the isotope is Co. In some arbitrary embodiments, the isotope is Cr. In some arbitrary embodiments, the isotope is Cu. In some arbitrary embodiments, the isotope is Fe. In some arbitrary embodiments, the isotope is Ge. In some arbitrary embodiments, the isotope is Hg. In some arbitrary aspects, the isotope is I. In some arbitrary aspects, the isotope is K. In some arbitrary aspects, the isotope is Li. In some arbitrary aspects, the isotope is Mg. In some arbitrary aspects, the isotope is Mn. In some arbitrary aspects, the isotope is Mo. In some arbitrary aspects, the isotope is Na. In some arbitrary aspects, the isotope is Ni. In some arbitrary aspects, the isotope is Pb. In some arbitrary aspects, the isotope is Rb. In some arbitrary aspects, the isotope is Ru. In some arbitrary aspects, the isotope is Se. In some arbitrary aspects, the isotope is Sn. In some arbitrary aspects, the isotope is Sr. In some arbitrary aspects, the isotope is Te. In some arbitrary aspects, the isotope is Ti. In some arbitrary aspects, the isotope is U. In some arbitrary aspects, the isotope is V. In some arbitrary embodiments, the isotope is Y. In some arbitrary embodiments, the isotope is Zn.

[0282] In some arbitrary aspects, the isotope is N, C, S, or O. In some arbitrary aspects, the isotope is N. In some arbitrary aspects, the isotope is C. In some arbitrary aspects, the isotope is S. In some arbitrary aspects, the isotope is O.

[0283] In some arbitrary embodiments, the isotope is Mo, Cr, Te, C, or Hg. In some arbitrary embodiments, the isotope is Mo. In some arbitrary embodiments, the isotope is Cr. In some arbitrary embodiments, the isotope is Te. In some arbitrary embodiments, the isotope is C. In some arbitrary embodiments, the isotope is Hg.

[0284] In any several embodiments, the at least one isotope feature is one isotope feature. In any several embodiments, the isotope of the at least one isotope feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 Contains Cr. In any several embodiments, the isotope of at least one isotopic feature is 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 68 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 87 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 88 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 86 Contains Sr. In any several embodiments, the isotope of at least one isotopic feature is 47Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 48 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 46 Contains Ti. In any several embodiments, the isotope of at least one isotopic feature is 50 Contains Cr. In any several embodiments, the isotope of at least one isotopic feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti and 50 Contains Cr.

[0285] In some arbitrary embodiments, the at least one isotope feature is a plurality of isotope features. In some arbitrary embodiments, the plurality of isotope features is about 2 to about 100 isotope features. In some arbitrary embodiments, the plurality of isotope features is 2 to 100 isotope features. In any several embodiment, the plurality of isotopic features include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 or more isotopic features. In any several embodiment, the plurality of isotopic features include at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopic features.

[0286] In any and all embodiments, the at least one isotopic feature is a plurality of isotopic features determined from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or more elements or isotopes. In any several embodiment, the plurality of isotopic features are determined from at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes.

[0287] In any several embodiments, the plurality of isotopic features include abundance and delta. In any several embodiments, abundance and delta values ​​are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of the isotope.

[0288] In any several embodiments, the plurality of isotopic features include abundance, atomic percentage, and / or isotopic ratio. In any several embodiments, abundance, atomic percentage, and / or isotopic ratios are obtained for at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or more isotopes of an isotope.

[0289] In any several embodiments, the isotopes include Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, and / or S. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Na, Mg, Al, P, K, Sc, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, I, Te, Ba, Hg, Pb, U, C, N, O, or S, or any combination of the aforementioned. In any and some embodiments, the element or isotope of the at least one isotopic feature includes Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S, or any combination of the aforementioned. In any several embodiments, the element or isotope of the at least one isotopic feature includes Li, B, Mg, Ca, Ti, Cr, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Mo, Ag, Sn, Ba, Hg, Pb, U, C, N, S, or any combination of the foregoing.

[0290] In any and some embodiments, the isotope contains Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, or Zn. In any and some embodiments, the isotope contains Ag. In any and some embodiments, the isotope contains Al. In any and some embodiments, the isotope contains B. In any and some embodiments, the isotope contains Ba. In any and some embodiments, the isotope contains Br. In any and some embodiments, the isotope contains Ca. In any and some embodiments, the isotope contains Co. In any and some embodiments, the isotope contains Cr. In any and some embodiments, the isotope contains Cu. In any and some embodiments, the isotope contains Fe. In any and some embodiments, the isotope contains Ge. In any and some embodiments, the isotope contains Hg. In some arbitrary embodiments, the isotope contains I. In some arbitrary embodiments, the isotope contains K. In some arbitrary embodiments, the isotope contains Li. In some arbitrary embodiments, the isotope contains Mg. In some arbitrary embodiments, the isotope contains Mn. In some arbitrary embodiments, the isotope contains Mo. In some arbitrary embodiments, the isotope contains Na. In some arbitrary embodiments, the isotope contains Ni. In some arbitrary embodiments, the isotope contains Pb. In some arbitrary embodiments, the isotope contains Rb. In some arbitrary embodiments, the isotope contains Ru. In some arbitrary embodiments, the isotope contains Se. In some arbitrary embodiments, the isotope contains Sn. In some arbitrary embodiments, the isotope contains Sr. In some arbitrary embodiments, the isotope contains Te. In some arbitrary embodiments, the isotope contains Ti. In some arbitrary embodiments, the isotope contains U. In some arbitrary embodiments, the isotope contains V. In some arbitrary embodiments, the isotope contains Y. In some arbitrary embodiments, the isotope contains Zn. In some arbitrary embodiments, the isotopes contain Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, and Zn.

[0291] In some arbitrary embodiments, the isotope contains N, C, S, and / or O. In some arbitrary embodiments, the isotope contains N. In some arbitrary embodiments, the isotope contains C. In some arbitrary embodiments, the isotope contains S. In some arbitrary embodiments, the isotope contains O. In some arbitrary embodiments, the isotope contains N, C, S, and O.

[0292] In any and some embodiments, the isotopes include Mo, Cr, Te, C and / or Hg. In any and some embodiments, the isotopes include Mo. In any and some embodiments, the isotopes include Cr. In any and some embodiments, the isotopes include Te. In any and some embodiments, the isotopes include C. In any and some embodiments, the isotopes include Hg. In any and some embodiments, the isotopes include Mo, Cr, Te, C and Hg.

[0293] In some arbitrary embodiments, at least one isotope feature includes a concentration ratio. In some arbitrary embodiments, the concentration ratio is N / C, S / C, O / C, S / N, N / O, or S / O. In some arbitrary embodiments, the concentration ratio is N / C. In some arbitrary embodiments, the concentration ratio is S / C. In some arbitrary embodiments, the concentration ratio is O / C. In some arbitrary embodiments, the concentration ratio is S / N. In some arbitrary embodiments, the concentration ratio is N / O. In some arbitrary embodiments, the concentration ratio is S / O.

[0294] In any several embodiments, the plurality of isotope features include at least one concentration ratio. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and / or S / O. In any several embodiments, the at least one concentration ratio includes N / C. In any several embodiments, the at least one concentration ratio includes S / C. In any several embodiments, the at least one concentration ratio includes O / C. In any several embodiments, the at least one concentration ratio includes S / N. In any several embodiments, the at least one concentration ratio includes N / O. In any several embodiments, the at least one concentration ratio includes S / O. In any several embodiments, the at least one concentration ratio includes N / C, S / C, O / C, S / N, N / O and S / O.

[0295] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (F34S), relative to stable isotopes of N 15 The composition includes at least one, two, three, four, five, six, seven, eight, nine, or ten of the following: the abundance of N, atomic percentage, or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0296] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio (F13C) of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of O. 18The abundance of O includes the ratio, atomic percentage, or isotopic ratio (F18O). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to nitrogen (SdN). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to carbon (NdC). In any several embodiments, the at least one isotopic feature includes the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio (f34S) of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of N. 15 This includes the abundance, atomic percentage, or isotopic ratio of N (F15N). In any several embodiments, the at least one isotopic feature includes the concentration ratio of oxygen to carbon (OdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of nitrogen to oxygen (NdO). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to carbon (SdC). In any several embodiments, the at least one isotopic feature includes the concentration ratio of sulfur to oxygen (SdO).

[0297] In any several embodiments, the at least one isotope feature is a stable isotope of C. 13 Abundance of C, atomic percentage or isotopic ratio (F13C), relative to stable isotopes of O 18 Abundance of O, atomic percentage or isotopic ratio (F18O), sulfur concentration ratio to nitrogen (SdN), nitrogen concentration ratio to carbon (NdC), and stable isotope ratio of S 34 Abundance of S, atomic percentage or isotopic ratio (f34S), relative to stable isotopes of N 15 This includes the abundance of N, atomic percentage or isotopic ratio (F15N), the concentration ratio of oxygen to carbon (OdC), the concentration ratio of nitrogen to oxygen (NdO), the concentration ratio of sulfur to carbon (SdC), and the concentration ratio of sulfur to oxygen (SdO).

[0298] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn, 67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and 98 Each of the isotopes of Mo contains at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more isotopic ratios, atomic percentages, or isotopic ratios of the stable isotopes of that isotope.

[0299] In any several embodiments, the at least one isotope feature is relative to a stable isotope of Ru. 101 This includes the abundance, atomic percentage, or isotopic ratio of Ru. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 107The abundance of Ag includes the relative abundance, atomic percentage, or isotopic ratio. In any several embodiments, the at least one isotopic feature includes the relative abundance, atomic percentage, or isotopic ratio of 10⁹Ag to the stable isotope of Ag. In any several embodiments, the at least one isotopic feature includes the relative abundance of B to the stable isotope of B 10 The abundance, atomic percentage, or isotopic ratio of B is included. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Sn. 118 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Sn. 119 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Sn. 124 This includes the abundance, atomic percentage, or isotopic ratio of Sn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Te. 124 This includes the abundance, atomic percentage, or isotopic ratio of Te. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 The abundance of I includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Te. 128 This includes the abundance, atomic percentage, or isotopic ratio of Te. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ba. 136 This includes the abundance, atomic percentage, or isotopic ratio of Ba. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ba. 137 This includes the abundance, atomic percentage, or isotopic ratio of Ba. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 235This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 238 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Na. 23 This includes the abundance, atomic percentage, or isotopic ratio of Na. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Al. 27 This includes the abundance, atomic percentage, or isotopic ratio of Al. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of K. 39 This includes the abundance, atomic percentage, or isotopic ratio of K. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ti. 48 This includes the abundance, atomic percentage, or isotopic ratio of Ti. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Cr. 54 This includes the abundance, atomic percentage, or isotopic ratio of Cr. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 54 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ni. 62 This includes the abundance, atomic percentage, or isotopic ratio of Ni. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Cu. 63This includes the abundance, atomic percentage, or isotopic ratio of Cu. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 66 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 68 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ge. 70 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ge. 74 This includes the abundance, atomic percentage, or isotopic ratio of Ge. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 76 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 77 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Br. 79 This includes the abundance, atomic percentage, or isotopic ratio of Br. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 80 The abundance of Se includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to a stable isotope of Se. 82 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Y. 89The abundance, atomic percentage, or isotopic ratio of Y is included. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 98 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0300] In any several embodiments, the at least one isotope feature is 101 Ru, 107 Ag, 109 Ag, 10 B, 118 Sn, 119 Sn, 124 Sn, 124 Te, 127 I, 128 Te, 136 Ba, 137 Ba, 13 C, 235 U, 238 U, 23 Na, 27 Al, 34 S, 39 K, 44 Ca, 48 Ti, 54 Cr, 54 Fe, 57 Fe, 62 Ni, 63 Cu, 66 Zn, 67 Zn, 68 Zn, 70 Enjoy, 74 Enjoy, 74 Se, 76 Se, 77 Se, 79 Br, 80 Se, 82 Se, 89 Y, 94 Mo, 97 Mo and 98This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0301] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al, 34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 Each of the isotopes of Mo contains at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more isotopic ratios, atomic percentages, or isotopic ratios of the isotope relative to its stable isotope.

[0302] In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 107 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ag. 109 This includes the abundance, atomic percentage, or isotopic ratio of Ag. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Sn. 118 The abundance of Sn includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 This includes the abundance, atomic percentage, or isotopic ratio of I. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of C. 13 This includes the abundance, atomic percentage, or isotopic ratio of C. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of U. 235 This includes the abundance, atomic percentage, or isotopic ratio of U. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Al. 27This includes the abundance, atomic percentage, or isotopic ratio of Al. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of S. 34 This includes the abundance, atomic percentage, or isotopic ratio of S. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Fe. 57 This includes the abundance, atomic percentage, or isotopic ratio of Fe. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Zn. 67 This includes the abundance, atomic percentage, or isotopic ratio of Zn. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Se. 74 This includes the abundance, atomic percentage, or isotopic ratio of Se. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 94 This includes the abundance, atomic percentage, or isotopic ratio of Mo. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Mo. 97 This includes the abundance, atomic percentage, or isotopic ratio of Mo.

[0303] In any several embodiments, the at least one isotope feature is 107 Ag, 109 Ag, 118 Sn, 127 I, 13 C, 235 U, 27 Al, 34 S, 44 Ca, 57 Fe, 67 Zn, 74 Se, 94 Mo and 97 This includes the abundance, atomic percentage, or isotopic ratio of each of the isotopes of Mo relative to the stable isotopes of that isotope.

[0304] In any several embodiments, the at least one isotope feature is relative to the stable isotope of I.127 The abundance of I includes atomic percentage or isotopic ratio. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of Ca. 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca. In any several embodiments, the at least one isotopic feature is relative to the stable isotope of I. 127 Abundance of I, atomic percentage or isotopic ratio, and relative to stable isotopes of Ca 44 This includes the abundance, atomic percentage, or isotopic ratio of Ca.

[0305] In any several embodiments, the isotope of the at least one isotope feature is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn,118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 Includes U, or any combination of the aforementioned.

[0306] In any and some embodiments, the isotope of the at least one isotope feature contains Zn. In any and some embodiments, the isotope of the at least one isotope feature is 64 Zn or 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Contains Zn. The isotope of at least one isotopic feature is 66 Contains Zn. In any several embodiments, the isotope of at least one isotopic feature is 64 Zn and 66 Contains Zn.

[0307] In any several embodiments, the isotope of the at least one isotope feature contains S. In any several embodiments, the isotope of the at least one isotope feature is 34 Includes S.

[0308] In any and some embodiments, the isotope of the at least one isotope feature includes N, C, S, or O. In any and some embodiments, the isotope of the at least one isotope feature includes N. In any and some embodiments, the isotope of the at least one isotope feature includes C. In any and some embodiments, the isotope of the at least one isotope feature includes S. In any and some embodiments, the isotope of the at least one isotope feature includes O. In any and some embodiments, the isotopes of the at least one isotope feature include N, C, S, and O.

[0309] In some arbitrary manner, a machine learning algorithm is a supervised machine learning algorithm.

[0310] In some arbitrary aspects, the predictive model is a classification model. In some arbitrary aspects, the predictive model is a binary classification model. In some arbitrary aspects, the predictive model is a multiclass classification model.

[0311] In some arbitrary aspects, the machine learning algorithm is an ensemble algorithm that includes logistic regression (e.g., ridge, lasso, or elastic net), naive Bayes classifier, Gaussian NB, Gaussian NB isotonic, Gaussian NB sigmoid, random forest classifier, gradient boosting classifier, XG boost classifier, histogram gradient boosting classifier, deep neural network classifier, decision tree classifier, bagging classifier, extra tree classifier, Ada Boost classifier, Cat Boost classifier, LGBM classifier, support vector classifier, perceptron classifier (e.g., multilayer perceptron classifier), deep learning classifier for tabular data (e.g., TabNet or TabML), passive-aggressive classifier or SGD classifier, or any combination of the aforementioned.

[0312] In some arbitrary aspects, the machine learning algorithm is a support vector classifier, a decision tree-based ensemble classifier, or a multilayer perceptron classifier.

[0313] In some arbitrary manner, the machine learning algorithm is an unsupervised machine learning algorithm.

[0314] In some arbitrary embodiments, the predictive model is a general model. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, and / or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, or has a different type of cancer compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects lives in a different geographical area compared to another of the multiple reference subjects. In some arbitrary embodiments, the multiple reference subjects originate from at least two, three, four, five, six or more different countries. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different sex compared to another of the multiple reference subjects. In some arbitrary embodiments, at least one of the multiple reference subjects is of a different age compared to another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects belongs to a different cultural or ethnic group than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different dietary habit than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects has a different type of cancer than another of the multiple reference subjects. In any several embodiments, at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different dietary habit, and has a different type of cancer than another of the multiple reference subjects.

[0315] In some arbitrary aspects, the predictive model is a specialized model. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and / or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, or has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area. In some arbitrary aspects, each of the multiple reference subjects is of the same sex. In some arbitrary aspects, each of the multiple reference subjects is male. In some arbitrary aspects, each of the multiple reference subjects is female. In some arbitrary aspects, each of the multiple reference subjects is of the same age. In some arbitrary aspects, each of the multiple reference subjects has the same type of cancer. In some arbitrary aspects, each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and has the same type of cancer.

[0316] In some arbitrary aspects, the plurality of reference objects are mammals. In some arbitrary aspects, the plurality of reference objects are humans.

[0317] In any and some aspects, the cancer is a blood cancer or a solid tumor. In any and some aspects, the cancer is a blood cancer. In any and some aspects, the blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some aspects, the blood cancer is lymphoma. In any and some aspects, the blood cancer is multiple myeloma. In any and some aspects, the blood cancer is leukemia. In any and some aspects, the cancer is a solid tumor.

[0318] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0319] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0320] In some arbitrary aspects, some of the reference objects are known to have cancer. In some arbitrary aspects, it is assumed that some of the reference objects do not have cancer. In some arbitrary aspects, some of the reference objects are known to have cancer, and it is assumed that some of the reference objects do not have cancer.

[0321] In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples. In some arbitrary embodiments, a machine learning algorithm is trained with a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples.

[0322] In some arbitrary embodiments, the label indicates whether the reference sample originates from a reference subject known to have cancer or from a reference subject assumed not to have cancer.

[0323] In some arbitrary aspects, the label indicates the characteristics of the reference cancer from which the acquired reference sample originates.

[0324] In some arbitrary embodiments, the label indicates the tumor burden level of the reference cancer from which the acquired reference sample originates.

[0325] In any several embodiments, the method further includes a step of preprocessing each of the at least one isotope feature values ​​before training a machine learning algorithm. In any several embodiments, the preprocessing step includes comparative comparison imputation of missing values. In any several embodiments, the preprocessing step includes replacing missing values ​​with zeros. In any several embodiments, the preprocessing step includes normalizing the data.

[0326] In some arbitrary aspects, cancer is detected in a subject if the predictive model indicates the presence of cancer.

[0327] In some arbitrary embodiments, the predictive model is constructed using a computer program product that is materially embodied in a non-temporary, machine-readable storage medium, which includes instructions configured to cause one or more data processors to construct the predictive model. In some arbitrary embodiments, the predictive model is trained using a computer program product that is materially embodied in a non-temporary, machine-readable storage medium, which includes instructions configured to cause one or more data processors to train the predictive model.

[0328] Furthermore, in some embodiments of this specification, a method is provided for determining the value of at least one isotopic feature of an isotope in a solid sample derived from a subject.

[0329] In any several embodiments, the method includes the step of determining the value of each of at least one isotopic feature of an isotope in a solid sample from the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in the solid sample from the subject. In any several embodiments, the at least one isotopic feature includes a normalized value of the concentration of the isotope measured in the solid sample from the subject.

[0330] Furthermore, in some embodiments herein, a method is provided for determining the value of at least one isotopic feature of an isotope in a solid sample of a subject, comprising: (a) detecting one or more ionized products of an isotope present in a solid sample of a subject by mass spectrometry, wherein the solid sample is optionally analyzed in the form of a liquid sample prepared by combustion or digestion of the solid sample, and the one or more ionized products are 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba,199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 (b) a step of determining the value of at least one isotope selected from U or any combination of the foregoing; and (b) a step of determining the value of at least one isotope feature, wherein the at least one isotope feature is the concentration of the at least one isotope or its normalized value.

[0331] In any and all embodiments, each value of the at least one isotope feature is a normalized value of the concentration of the at least one isotope.

[0332] In any and all embodiments, the method further includes normalizing the values ​​of each of the at least one isotopic features.

[0333] In any and some embodiments, the normalized value of each of the at least one isotopic feature is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination of the aforementioned. In any and some embodiments, the normalized value of each of the at least one isotopic feature is an atomic percentage (atomic%). In any and some embodiments, the normalized value of each of the at least one isotopic feature is an abundance (F). In any and some embodiments, the normalized value of each of the at least one isotopic feature is an isotopic ratio (R). In any and some embodiments, the normalized value of each of the at least one isotopic feature is a delta value (delta).

[0334] Also provided herein is a method for determining the value of at least one isotope feature of an isotope in a solid sample derived from a subject, comprising the following: (a) detecting, by mass spectrometry, one or more ionization products of an isotope element present in a solid sample derived from a subject, optionally the solid sample being analyzed in the state of a liquid sample prepared through combustion or by digestion of the solid sample, and the one or more ionization products being 13 C, 34 S, 10 B, 11 B, 24 ]>Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 U, or at least one isotope selected from any combination of the foregoing, step; (b) determining a value of at least one isotope feature, wherein the at least one isotope feature is the concentration of the at least one isotope, step; (c) normalizing the value of the concentration of the at least one isotope, wherein each normalized value of the at least one isotope feature is atomic percentage (atomic %), abundance ratio (F), isotope ratio (R), or delta value (delta), or any combination of the foregoing, step.

[0335] Also provided herein is a method for determining a value of at least one isotope feature of an isotope in a solid sample from a subject, comprising: (a) detecting, by mass spectrometry, one or more ionized products of an isotopic element present in the solid sample from the subject, wherein the solid sample is analyzed in a liquid sample state prepared by combustion or by digestion of the solid sample, and the one or more ionized products are 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, <� 63 Cu, 65 Cu,64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 (b) a step of determining a value for at least one isotope selected from U or any combination of the foregoing; (c) a step of normalizing the value which is the concentration of the at least one isotope, wherein the normalized value for each of the at least one isotope is an atomic percentage (atomic%), abundance (F), isotope ratio (R), delta value (delta), or any combination of the foregoing.

[0336] In any and some embodiments, the normalized value of each of the at least one isotopic feature is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination of the aforementioned. In any and some embodiments, the normalized value of each of the at least one isotopic feature is an atomic percentage (atomic%). In any and some embodiments, the normalized value of each of the at least one isotopic feature is an abundance (F). In any and some embodiments, the normalized value of each of the at least one isotopic feature is an isotopic ratio (R). In any and some embodiments, the normalized value of each of the at least one isotopic feature is a delta value (delta).

[0337] In any several embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In any several embodiments, the delta value is the conventional delta value, the study-specific delta value (InH), the dissimilar simple nitrogen-based delta value (simple Nδ), or the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

[0338] In any several embodiments, determining the value of each of the at least one isotopic feature involves a reference to one or more isotopic features present in a standard material including a plurality of calibration standards and a blank standard. In any several embodiments, determining the value of each of the at least one isotopic feature involves a reference to one or more isotopic features present in a standard material including an internal standard and / or a reference material. In any several embodiments, determining the value of each of the at least one isotopic feature involves a reference to one or more isotopic features present in a standard material including an internal standard. In any several embodiments, determining the value of each of the at least one isotopic feature involves a reference to one or more isotopic features present in a standard material including a reference material. In any several embodiments, determining the value of each of the at least one isotopic feature involves a reference to one or more isotopic features present in a standard material including an internal standard and a reference material.

[0339] In any several embodiments, the method further includes introducing a standard substance into a mass spectrometer, where the mass spectrometer detects one or more ionized products of isotopes present in the standard substance and the prepared liquid or solid sample. In any several embodiments, the method further includes introducing a standard substance into a mass spectrometer, where the mass spectrometer detects one or more ionized products of isotopes present in the standard substance and the prepared liquid sample. In any several embodiments, the method further includes introducing a standard substance into a mass spectrometer, where the mass spectrometer detects one or more ionized products of isotopes present in the standard substance and the solid sample.

[0340] In some arbitrary embodiments, the sample includes keratinous tissue, optionally hard keratinous tissue. In some arbitrary embodiments, the sample includes hard keratinous tissue. In some arbitrary embodiments, the sample is a non-invasive sample, optionally a hair sample or a nail sample. In some arbitrary embodiments, the sample is a hair sample. In some arbitrary embodiments, the sample is a nail sample.

[0341] In some arbitrary embodiments, digestion is by acid digestion and / or thermal digestion. In some arbitrary embodiments, digestion is by acid digestion. In some arbitrary embodiments, digestion is by thermal digestion. In some arbitrary embodiments, digestion is by acid digestion and thermal digestion. In some arbitrary embodiments, acid digestion uses nitric acid (HNO3). In some arbitrary embodiments, acid digestion uses nitric acid (HNO3) and hydrogen peroxide (H2O2). In some arbitrary embodiments, thermal digestion is by microwave.

[0342] In some arbitrary embodiments, the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS). In some arbitrary embodiments, the ICP-MS is performed using kinetic energy discrimination (KED).

[0343] In any several embodiments, the at least one isotope is one isotope. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 It is Cr. In any several embodiments, the at least one isotope is 66Zn. In any several embodiments, the at least one isotope is 64 It is Zn. In any several embodiments, the at least one isotope is 68 It is Zn. In any several embodiments, the at least one isotope is 87It is Sr. In any several embodiments, the at least one isotope is 88 It is Sr. In any several embodiments, the at least one isotope is 86 It is Sr. In any several embodiments, the at least one isotope is 47 It is Ti. In any several embodiments, the at least one isotope is 48 It is Ti. In any several embodiments, the at least one isotope is 46 It is Ti. In any several embodiments, the at least one isotope is 50 It is Cr. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti and 50 It is Cr.

[0344] In any and some embodiments, the isotope 64 Zn, 66 Zn, or 34 It is S. In any several embodiment, the isotope is 64 It is Zn. In some arbitrary aspects, the isotope is 66 It is Zn. In some arbitrary aspects, the isotope is 34 It is S.

[0345] In some arbitrary embodiments, the at least one isotope is multiple isotopes. In some arbitrary embodiments, the multiple isotopes are about 2 to about 59 isotopes. In some arbitrary embodiments, the multiple isotopes are 2 to 59 isotopes. In any several embodiment, the at least one isotope is one of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59 isotopes.

[0346] In some of the embodiments, the subject is suspected of having cancer.

[0347] In any and some embodiments, the method further includes selecting subjects suspected of having cancer.

[0348] In any and some embodiments, the subject has one or more symptoms that indicate the presence of cancer in the subject.

[0349] In any and some embodiments, cancer is a blood cancer or a solid tumor. In any and some embodiments, blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some embodiments, blood cancer is leukemia. In any and some embodiments, blood cancer is lymphoma. In any and some embodiments, blood cancer is multiple myeloma.

[0350] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0351] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0352] In some arbitrary aspects, the subject is a mammal. In some arbitrary aspects, the subject is a human.

[0353] In some of its embodiments, the method is non-invasive.

[0354] Furthermore, this specification provides a kit for determining the value of at least one isotopic feature in a sample derived from a target.

[0355] In any several embodiments, the values ​​of each of at least one isotopic feature of the isotope in the subject. In any several embodiments, the at least one isotopic feature includes the concentration of the isotope measured in a sample from the subject. In any several embodiments, the at least one isotopic feature includes the normalized value of the concentration of the isotope measured in a sample from the subject.

[0356] Furthermore, in this specification, a kit for determining the value of at least one isotopic feature in a sample derived from a target, wherein the at least one isotopic feature is the concentration or normalized value of at least one isotope, and the at least one isotope is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, 238 The kit is selected from U or any combination of the foregoing, and comprises (i) a reference material containing a validated standard material for the at least one isotope; (ii) packaging material; and (iii) instructions for using the kit, for determining the value of the at least one isotopic feature.

[0357] In some arbitrary embodiments, each value of the at least one isotope feature is a normalized value of the concentration of the at least one isotope. In some arbitrary embodiments, each normalized value of the at least one isotope feature is an atomic percentage (atomic %), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination of the aforementioned. In some arbitrary embodiments, each normalized value of the at least one isotope feature is an atomic percentage (atomic %). In some arbitrary embodiments, each normalized value of the at least one isotope feature is an abundance (F). In some arbitrary embodiments, each normalized value of the at least one isotope feature is an isotopic ratio (R). In some arbitrary embodiments, each normalized value of the at least one isotope feature is a delta value (delta).

[0358] In any several embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In any several embodiments, the delta value is the conventional delta value, the study-specific delta value (InH), the dissimilar simple nitrogen-based delta value (simple Nδ), or the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ). In any several embodiments, the delta value is the conventional delta value. In any several embodiments, the delta value is the study-specific delta value (InH). In any several embodiments, the delta value is the dissimilar simple nitrogen-based delta value (simple Nδ). In any several embodiments, the delta value is the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

[0359] In any and all embodiments, each value of the at least one isotopic feature is associated with a reference to one or more isotopic features present in the reference material.

[0360] In any and some embodiments, the kit further comprises a calibration standard, a blank standard, and / or an internal standard. In any and some embodiments, the kit further comprises a calibration standard. In any and some embodiments, the kit further comprises a blank standard. In any and some embodiments, the kit further comprises an internal standard. In any and some embodiments, the kit further comprises a calibration standard, a blank standard, and an internal standard.

[0361] In some arbitrary embodiments, the sample includes keratinous tissue, optionally hard keratinous tissue. In some arbitrary embodiments, the sample includes hard keratinous tissue. In some arbitrary embodiments, the sample is a non-invasive sample, optionally a hair sample or a nail sample. In some arbitrary embodiments, the sample is a hair sample. In some arbitrary embodiments, the sample is a nail sample.

[0362] In some arbitrary aspects, the sample is a digested sample. In some arbitrary aspects, the kit includes instructions for use in digesting the sample. In some arbitrary aspects, digestion is by acid digestion and / or thermal digestion. In some arbitrary aspects, digestion is by acid digestion. In some arbitrary aspects, digestion is by thermal digestion. In some arbitrary aspects, digestion is by acid digestion and thermal digestion. In some arbitrary aspects, acid digestion is performed using nitric acid (HNO3). In some arbitrary aspects, acid digestion is performed using nitric acid (HNO3) and hydrogen peroxide (H2O2). In some arbitrary aspects, thermal digestion is performed by microwave.

[0363] In any several embodiments, the at least one isotope is one isotope. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr,47 Ti, 48 Ti, 46 Ti, and / or 50 It is Cr. In any several embodiments, the at least one isotope is 66Zn. In any several embodiments, the at least one isotope is 64 It is Zn. In any several embodiments, the at least one isotope is 68 It is Zn. In any several embodiments, the at least one isotope is 87 It is Sr. In any several embodiments, the at least one isotope is 88 It is Sr. In any several embodiments, the at least one isotope is 86 It is Sr. In any several embodiments, the at least one isotope is 47 It is Ti. In any several embodiments, the at least one isotope is 48 It is Ti. In any several embodiments, the at least one isotope is 46 It is Ti. In any several embodiments, the at least one isotope is 50 It is Cr. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and 50 It is Cr.

[0364] In any and some embodiments, the isotope 64 Zn, 66 Zn, or 34 It is S. In any several embodiment, the isotope is 64 It is Zn. In some arbitrary aspects, the isotope is 66 It is Zn. In some arbitrary aspects, the isotope is 34 It is S.

[0365] In some arbitrary embodiments, the at least one isotope is multiple isotopes. In some arbitrary embodiments, the multiple isotopes are about 2 to about 59 isotopes. In some arbitrary embodiments, the multiple isotopes are 2 to 59 isotopes. In any several embodiment, the at least one isotope is one of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59 isotopes.

[0366] In some of the embodiments, the subject is suspected of having cancer.

[0367] In any and some embodiments, the method further includes selecting subjects suspected of having cancer.

[0368] In any and some embodiments, the subject has one or more symptoms that indicate the presence of cancer in the subject.

[0369] In any and some embodiments, cancer is a blood cancer or a solid tumor. In any and some embodiments, blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some embodiments, blood cancer is leukemia. In any and some embodiments, blood cancer is lymphoma. In any and some embodiments, blood cancer is multiple myeloma.

[0370] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0371] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0372] In some arbitrary aspects, the subject is a mammal. In some arbitrary aspects, the subject is a human.

[0373] Furthermore, this specification provides a method for determining the value of at least one isotopic feature of a sample derived from a subject, comprising the step of using a kit of any embodiment in accordance with the instructions for use.

[0374] Furthermore, in this specification, isotope profiles identified by mass spectrometry are isotope profiles derived from solid samples of the subject, 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The present invention provides an isotopic profile that includes the value of at least one isotopic feature of at least one isotope selected from U or any combination of the aforementioned, wherein the at least one isotopic feature is the concentration of the at least one isotope. Also, in this specification, an isotopic profile identified by mass spectrometry, which is an isotopic profile derived from a solid sample of the subject, 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu,65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The present invention provides an isotopic profile that includes a value for at least one isotopic feature of at least one isotope selected from U or any combination of the foregoing, wherein the at least one isotopic feature is the concentration of the at least one isotope or a normalized value of the concentration of the at least one isotope.

[0375] In some arbitrary embodiments, the value of the at least one isotope feature is the normalized value of the concentration of the at least one isotope. In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the atomic percentage (atomic%), abundance (F), isotope ratio (R), or delta value (delta), or any combination of the foregoing. In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the atomic percentage (atomic%). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the abundance (F). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the isotope ratio (R). In some arbitrary embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta).

[0376] In any several embodiments, the normalized value of each of the at least one isotope feature is the delta value (delta). In any several embodiments, the delta value is the conventional delta value, the study-specific delta value (InH), the dissimilar simple nitrogen-based delta value (simple Nδ), or the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ). In any several embodiments, the delta value is the conventional delta value. In any several embodiments, the delta value is the study-specific delta value (InH). In any several embodiments, the delta value is the dissimilar simple nitrogen-based delta value (simple Nδ). In any several embodiments, the delta value is the dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

[0377] In some arbitrary embodiments, the sample includes keratinous tissue, optionally hard keratinous tissue. In some arbitrary embodiments, the sample includes hard keratinous tissue. In some arbitrary embodiments, the sample is a non-invasive sample, optionally a hair sample or a nail sample. In some arbitrary embodiments, the sample is a hair sample. In some arbitrary embodiments, the sample is a nail sample.

[0378] In some arbitrary embodiments, a solid sample is digested for mass spectrometry to prepare a liquid sample. In some arbitrary embodiments, digestion is by acid digestion and / or thermal digestion. In some arbitrary embodiments, digestion is by acid digestion. In some arbitrary embodiments, digestion is by thermal digestion. In some arbitrary embodiments, digestion is by acid digestion and thermal digestion. In some arbitrary embodiments, acid digestion uses nitric acid (HNO3). In some arbitrary embodiments, acid digestion uses nitric acid (HNO3) and hydrogen peroxide (H2O2). In some arbitrary embodiments, thermal digestion is by microwave.

[0379] In some arbitrary embodiments, the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS). In some arbitrary embodiments, the ICP-MS is performed using kinetic energy discrimination (KED).

[0380] In any several embodiments, the at least one isotope is one isotope. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 It is Cr. In any several embodiments, the at least one isotope is 66Zn. In any several embodiments, the at least one isotope is 64 It is Zn. In any several embodiments, the at least one isotope is 68 It is Zn. In any several embodiments, the at least one isotope is 87 It is Sr. In any several embodiments, the at least one isotope is 88 It is Sr. In any several embodiments, the at least one isotope is 86 It is Sr. In any several embodiments, the at least one isotope is 47It is Ti. In any several embodiments, the at least one isotope is 48 It is Ti. In any several embodiments, the at least one isotope is 46 It is Ti. In any several embodiments, the at least one isotope is 50 It is Cr. In any several embodiments, the at least one isotope is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and 50 It is Cr.

[0381] In any and some embodiments, the isotope 64 Zn, 66 Zn, or 34 It is S. In any several embodiment, the isotope is 64 It is Zn. In some arbitrary aspects, the isotope is 66 It is Zn. In some arbitrary aspects, the isotope is 34 It is S.

[0382] In some arbitrary embodiments, the at least one isotope is multiple isotopes. In some arbitrary embodiments, the multiple isotopes are about 2 to about 59 isotopes. In some arbitrary embodiments, the multiple isotopes are 2 to 59 isotopes. In any several embodiment, the at least one isotope is one of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59 isotopes.

[0383] In some of the embodiments, the subject is suspected of having cancer.

[0384] In any and some embodiments, the method further includes selecting subjects suspected of having cancer.

[0385] In any and some embodiments, the subject has one or more symptoms that indicate the presence of cancer in the subject.

[0386] In any and some embodiments, cancer is a blood cancer or a solid tumor. In any and some embodiments, blood cancer is leukemia, lymphoma, or multiple myeloma. In any and some embodiments, blood cancer is leukemia. In any and some embodiments, blood cancer is lymphoma. In any and some embodiments, blood cancer is multiple myeloma.

[0387] In any given form, cancer includes lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, and non-Hodgkin's disease. Waldenström macroglobulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma Fetal cancer, Wilms' tumor, cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer The following are selected from the group consisting of (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary cancer, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil cancer, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal tract / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland cancer, brain cancer, and malignant peripheral nerve sheath tumors (MPNST) associated with neurofibromatosis type 1.

[0388] In any given manner, the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia. In any given manner, the cancer is prostate cancer. In any given manner, the cancer is bladder cancer. In any given manner, the cancer is kidney cancer. In any given manner, the cancer is head and neck cancer. In any given manner, the cancer is breast cancer. In any given manner, the cancer is leukemia.

[0389] In some arbitrary aspects, the subject is a mammal. In some arbitrary aspects, the subject is a human.

[0390] Furthermore, this specification provides a method for determining whether a subject has cancer, comprising the step of comparing one or more isotopic features of an isotopic profile of any embodiment with one or more corresponding isotopic features of (i) an individual characterized as having cancer, (ii) an individual characterized as not having cancer, or (iii) an individual characterized as having cancer and an individual characterized as not having cancer.

[0391] In any several embodiments, the method includes the step of comparing one or more isotope features of the isotope profile of any embodiment with one or more corresponding isotope features of (i) individuals characterized as having cancer, (ii) individuals characterized as not having cancer, or (iii) individuals characterized as having cancer and individuals characterized as not having cancer. In any several embodiments, the method includes the step of comparing one or more isotope features of the isotope profile of any embodiment with one or more corresponding isotope features of individuals characterized as having cancer. In any several embodiments, the method includes the step of comparing one or more isotope features of the isotope profile of any embodiment with one or more corresponding isotope features of individuals characterized as not having cancer. In any several embodiments, the method includes the step of comparing one or more isotope features of the isotope profile of any emb...

Claims

1. Methods for detecting whether a subject has cancer include the following: (a) A step of determining the value of each of at least one isotopic feature in a sample of the subject, wherein the value of each of the at least one isotopic feature is the normalized value of (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of inputting the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of determining whether the subject has the cancer using the predictive model.

2. Methods for detecting cancer in a subject, including the following: (a) A step of determining the value of each of at least one isotopic feature in a sample of the subject, wherein the value of each of the at least one isotopic feature is the normalized value of (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of inputting the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of detecting the cancer in the subject using the predictive model.

3. Methods for assessing the characteristics of cancer in a subject, including the following: (a) A step of determining the value of each of at least one isotopic feature in a sample of the subject, wherein the value of each of the at least one isotopic feature is the normalized value of (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of inputting the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict cancer characteristics in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of determining the characteristics of cancer in the subject using the predictive model.

4. The method according to claim 3, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

5. The method according to any one of claims 1 to 4, wherein the sample is a solid sample, and the step of determining the value of each of the at least one isotopic features includes mass spectrometry analysis of the solid sample which is optionally analyzed via combustion, or of a liquid sample which is prepared by digestion of the solid sample.

6. The method according to any one of claims 1 to 5, wherein the value is a normalized value, and the step of determining the value of each of the at least one isotopic features includes calculating the normalized value from the mass spectrometry analysis.

7. The step of determining the value of each of the at least one isotopic feature, provided that the sample is a solid sample. (i) Preparing a liquid sample of the solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (ii) Introducing the prepared liquid sample into a mass spectrometer to detect one or more ionization products of elements and isotopes present in the prepared liquid sample; and (iii) Analyzing the element and one or more ionized products of the detected isotopes in order to create a value for each of the at least one isotopic feature. A method according to any one of claims 1 to 6, including the method described in any one of claims 1 to 6.

8. The step of determining the value of each of the at least one isotopic feature, provided that the sample is a solid sample. (i) preparing the solid sample for analysis by mass spectrometry; (ii) Introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of elements and isotopes present in the prepared solid sample, wherein the detection optionally includes combustion of the prepared solid sample; and (iii) Analyzing the element and one or more ionized products of the detected isotopes in order to create a value for each of the at least one isotopic feature. A method according to any one of claims 1 to 6, including the method described in any one of claims 1 to 6.

9. The method according to any one of claims 1 to 8, wherein the decision in step (a), the input in step (b), and / or the decision in step (c) are performed by a processor of a computing device.

10. Methods for detecting whether a subject has cancer include the following: (a) A step of receiving values ​​for each of at least one isotopic features for a sample of the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of using at least one processor to input the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of determining whether the subject has the cancer using the predictive model.

11. Methods for detecting cancer in a subject, including the following: (a) A step of receiving values ​​for each of at least one isotopic features for a sample of the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of using at least one processor to input the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict the presence or absence of cancer in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of detecting the cancer in the subject using the predictive model.

12. Methods for assessing the characteristics of cancer in a subject, including the following: (a) A step of receiving values ​​for each of at least one isotopic features for a sample of the subject using one or more processors, wherein each of the values ​​for the at least one isotopic features is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in the sample of the subject; (b) A step of using at least one processor to input the values ​​of each of the at least one isotopic features derived from the subject into a predictive model configured to predict cancer characteristics in the subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects; and (c) The step of determining the characteristics of cancer in the subject using the predictive model.

13. The method according to claim 12, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

14. The method according to any one of claims 10 to 13, wherein the sample is a solid sample, and the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of the solid sample, which is optionally analyzed via combustion, or of a liquid sample prepared by digestion of the solid sample.

15. The method according to any one of claims 10 to 14, wherein the value is a normalized value, and the step of determining the value of each of the at least one isotopic feature includes calculating the normalized value from mass spectrometry analysis.

16. The sample is a solid sample, and the method includes, prior to step (a), a step of determining the value of each of the at least one isotopic feature, (i) Preparing a liquid sample of the solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (ii) Introducing the prepared liquid sample into a mass spectrometer to detect one or more ionized products of elemental isotopes present in the prepared liquid sample; and (iii) Analyzing the element and one or more ionized products of the detected isotopes in order to create a value for each of the at least one isotopic feature. The method according to any one of claims 10 to 15, including the method described in any one of claims 10 to 15.

17. The sample is a solid sample, and the method includes, prior to step (a), a step of determining the value of each of the at least one isotopic feature, (i) preparing the solid sample for analysis by mass spectrometry; (ii) Introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of elements and isotopes present in the prepared solid sample, wherein the detection optionally includes combustion of the prepared solid sample; and (iii) Analyzing the element and one or more ionized products of the detected isotopes in order to create a value for each of the at least one isotopic feature. The method according to any one of claims 10 to 15, including the method described in any one of claims 10 to 15.

18. The method according to any one of claims 7 to 9, 16, and 17, wherein the analysis of one or more ionized products of elements and isotopes detected is accompanied by a reference to one or more ionized products of elements and isotopes present in a standard material comprising a plurality of calibration standard materials and a blank standard material.

19. The method according to any one of claims 7 to 9 and 16 to 18, wherein the analysis of one or more ionized products of an element and an isotope that have been detected is accompanied by a reference to one or more ionized products of an element and an isotope that are present in a standard material including an internal standard and / or a reference material.

20. The method according to claim 18 or 19, wherein the standard substance is further introduced into a mass spectrometer, and the mass spectrometer detects one or more ionized products of elements and isotopes present in the standard substance and the prepared liquid or solid sample.

21. Methods for detecting whether a subject has cancer include the following: (a) The step of preparing the solid sample to be obtained from the subject; (b) A step of preparing a liquid sample or a solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) The step of preparing a standard material including multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) The step of introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately in order to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) A step of analyzing one or more ionized products of an element and its isotopes to create a value for each of the at least one isotopic feature, wherein the value for each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) A step in which one or more processors receive the respective values ​​of the at least one isotope feature; (g) A step of using at least one processor to input the values ​​of each of the at least one isotopic feature into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) Using the predictive model, determine whether the subject has the cancer.

22. Methods for detecting cancer in a subject, including the following: (a) The step of preparing the solid sample to be obtained from the subject; (b) A step of preparing a liquid sample or a solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) The step of preparing a standard material including multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) The step of introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately in order to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) A step of analyzing one or more ionized products of an element and its isotopes to create a value for each of the at least one isotopic feature, wherein the value for each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) A step in which one or more processors receive the respective values ​​of the at least one isotope feature; (g) A step of using at least one processor to input the values ​​of each of the at least one isotopic feature into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) The step of detecting the cancer in the subject using the predictive model.

23. Methods for assessing the characteristics of cancer in a subject, including the following: (a) The step of preparing the solid sample to be obtained from the subject; (b) A step of preparing a liquid sample or a solid sample for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of the solid sample; (c) The step of preparing a standard material including multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) The step of introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately in order to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) A step of analyzing one or more ionized products of an element and its isotopes to create a value for each of the at least one isotopic feature, wherein the value for each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) A step in which one or more processors receive the respective values ​​of the at least one isotope feature; (g) A step of using at least one processor to input the values ​​of each of the at least one isotopic feature into a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of each of the at least one isotopic feature from a plurality of reference samples from a plurality of reference subjects; and (h) Using the predictive model to determine the characteristics of cancer in the subject.

24. The method according to claim 23, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

25. The method according to any one of claims 21 to 24, wherein the value is a normalized value, and the step of determining the value of each of the at least one isotopic features includes calculating the normalized value from mass spectrometry analysis.

26. The method according to any one of claims 1 to 25, wherein the sample comprises keratinous tissue, optionally hard keratinous tissue.

27. The method according to any one of claims 1 to 26, wherein the sample is a non-invasive sample.

28. The method according to any one of claims 1 to 27, wherein the sample is a hair sample or a nail sample.

29. The method according to any one of claims 5, 7, 8, 14, 16, and 17-28, wherein the digestion is by acid digestion and / or thermal digestion, and optionally the digestion is by acid digestion and thermal digestion.

30. The aforementioned acid digestion is nitric acid (HNO 3 ) or nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 The method according to claim 29, wherein the method uses the following:

31. The method according to any one of claims 29 to 30, wherein the thermal digestion is performed by microwaves.

32. The method according to any one of claims 5 to 9 and 14 to 31, wherein the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS), and optionally the ICP-MS is performed using kinetic energy discrimination (KED).

33. The method according to any one of claims 1 to 32, wherein the value of each of the at least one isotope feature is a normalized value of any one of (1) to (3).

34. The method according to any one of claims 1 to 33, wherein each of the values ​​of the at least one isotopic feature is a normalized value of the concentration of the isotope.

35. The method according to any one of claims 1 to 33, wherein each of the values ​​of the at least one isotopic feature is a normalized value of the concentration of the isotope of the isotope element.

36. The method according to any one of claims 1 to 35, wherein the normalized value for each of the at least one isotope feature is an atomic percentage (atomic%), abundance (F), isotope ratio (R), concentration ratio (CR), or delta value (delta), or any combination thereof.

37. The method according to any one of claims 1 to 36, wherein the normalized value of each of the at least one isotopic features is the abundance (F).

38. The method according to any one of claims 1 to 36, wherein the normalized value of each of the at least one isotope feature is the delta value.

39. The method according to claim 36 or 38, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

40. The method according to any one of claims 1 to 39, wherein the at least one isotope feature is one isotope feature.

41. The isotope of the at least one isotope feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 Cr, and the method according to any one of claims 1 to 40.

42. The method according to any one of claims 1 to 39, wherein the at least one isotope feature is a plurality of isotope features.

43. The method according to claim 42, wherein the plurality of isotope features are 2 to 100 isotope features, and optionally the plurality of isotope features include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 or more isotope features.

44. The method according to claim 42 or 43, wherein the at least one isotopic feature is a plurality of isotopic features determined from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or more elements or isotopes.

45. The method according to any one of claims 1 to 44, wherein the element or isotope of the at least one isotopic feature includes Li, B, Na, Mg, Al, P, K, Sc, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, I, Te, Ba, Hg, Pb, U, C, N, O, or S, or any combination of the foregoing.

46. The method according to any one of claims 1 to 45, wherein the element or isotope of the at least one isotopic feature includes Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S, or any combination of the foregoing.

47. The method according to any one of claims 1 to 46, wherein the element or isotope of the at least one isotopic feature includes Li, B, Mg, Ca, Ti, Cr, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Mo, Ag, Sn, Ba, Hg, Pb, U, C, N, S, or any combination of the aforementioned.

48. The isotope of the at least one isotopic feature is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The method according to any one of claims 1 to 47, comprising U, or any combination thereof.

49. The method according to any one of claims 1 to 48, wherein the isotope of at least one isotopic feature comprises Zn.

50. The isotope of the at least one isotope feature is 64 Zn and / or 66 A method according to any one of claims 1 to 49, comprising Zn.

51. The method according to any one of claims 1 to 48, wherein the isotope of at least one isotopic feature comprises N, C, S, or O.

52. The isotope of the at least one isotope feature is 34 The method according to any one of claims 1 to 48 and 51, comprising S.

53. The method according to any one of claims 1 to 52, wherein the machine learning algorithm is a supervised machine learning algorithm.

54. The method according to any one of claims 1 to 3, 5 to 11, 13 to 21, and 23 to 53, wherein the prediction model is a classification model.

55. The method according to any one of claims 1 to 3, 5 to 11, 13 to 21, and 23 to 54, wherein the prediction model is a binary classification model.

56. The method according to any one of claims 1 to 3, 5 to 11, 13 to 21, and 23 to 54, wherein the predictive model is a multi-class classification model.

57. The method according to any one of claims 1 to 3, 5 to 11, 13 to 21 and 23 to 56, wherein the machine learning algorithm is an ensemble algorithm including logistic regression (e.g., ridge, lasso, or elastic net), naive Bayes classifier, Gaussian NB, Gaussian NB isotonic, Gaussian NB sigmoid, random forest classifier, gradient boosting classifier, XG boost classifier, histogram gradient boosting classifier, deep neural network classifier, decision tree classifier, bagging classifier, extra tree classifier, Ada Boost classifier, Cat Boost classifier, LGBM classifier, support vector classifier, perceptron classifier (e.g., multilayer perceptron classifier), deep learning classifier for tabular data (e.g., TabNet or TabML), passive aggressive classifier or SGD classifier, or any combination of the foregoing.

58. The method according to any one of claims 1 to 3, 5 to 11, 13 to 21, and 23 to 57, wherein the machine learning algorithm is a support vector classifier, a decision tree-based ensemble classifier, or a multilayer perceptron classifier.

59. The method according to any one of claims 3 to 8, 11 to 18, and 21 to 53, wherein the predictive model is a regression model.

60. The method according to any one of claims 1 to 52, wherein the machine learning algorithm is an unsupervised machine learning algorithm.

61. The method according to any one of claims 1 to 60, wherein the prediction model is a general model.

62. The method according to claim 61, wherein at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, comes from a different cultural or ethnic group, has a different diet, and / or has a different type of cancer compared to another of the multiple reference subjects.

63. The method according to claim 62, wherein the plurality of references originate from at least two, three, four, five, six or more different countries.

64. The method according to any one of claims 61 to 63, wherein at least one of the multiple reference subjects is of a different sex from another of the multiple reference subjects.

65. The method according to any one of claims 1 to 60, wherein the prediction model is a specialized model.

66. The method according to claim 65, wherein each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and / or has the same type of cancer.

67. The method according to claim 65 or claim 66, wherein each of the multiple reference objects is of the same sex.

68. The method according to any one of claims 65 to 67, wherein each of the multiple reference subjects is male.

69. The method according to any one of claims 65 to 67, wherein each of the multiple reference subjects is female.

70. The method according to any one of claims 65 to 69, wherein each of the multiple reference targets has the same type of cancer.

71. The method according to any one of claims 1 to 70, wherein the subject is a mammal.

72. The method according to any one of claims 1 to 71, wherein the subject is a human.

73. The method according to any one of claims 1 to 72, wherein the cancer is a blood cancer or a solid tumor.

74. The method according to claim 73, wherein the blood cancer is leukemia, lymphoma, or multiple myeloma.

75. The aforementioned cancers include lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, non-Hodgkin's disease, and Waldenström macrocytosis. Globulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal carcinoma, Wilms' tumor, uterine Cervical cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial cancer, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), epithelial ovarian cancer, ovarian cancer The method according to any one of claims 1 to 74, selected from the group consisting of fallopian tube cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary carcinoma, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil carcinoma, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal / stomach (GIST) cancer, lymphoma, squamous cell carcinoma of the head and neck (SCCHN), salivary gland carcinoma, brain cancer, and malignant peripheral nerve sheath tumor (MPNST) associated with neurofibromatosis type 1.

76. The method according to any one of claims 1 to 75, wherein the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia.

77. A non-invasive method according to any one of claims 1 to 76.

78. The aforementioned prediction model, (i) determining the value of each of the at least one isotopic feature for the plurality of reference samples; and (ii) Training the machine learning algorithm using the values ​​of each of the at least one isotopic features for the plurality of reference samples. The method according to any one of claims 1 to 77, constructed by...

79. The method according to any one of claims 1 to 78, wherein some of the aforementioned reference subjects are known to have cancer, and it is assumed that some of the aforementioned reference subjects do not have cancer.

80. The method according to any one of claims 1 to 59 and 61 to 79, wherein the machine learning algorithm is trained using a plurality of labels, the plurality of labels including a label for each of the plurality of reference samples.

81. The method according to claim 80, wherein the label indicates whether the reference sample is derived from a reference subject known to have cancer or from a reference subject assumed not to have cancer.

82. The method according to claim 80, wherein the label indicates the characteristics of the reference cancer from which the acquired reference sample originates.

83. The method according to claim 80 or 82, wherein the label indicates the tumor volume level of the reference cancer from which the acquired reference sample originates.

84. The method according to any one of claims 1 to 83, further comprising the step of training the machine learning algorithm using the values ​​of each of the at least one isotopic features for the plurality of reference samples prior to step (a).

85. The method according to any one of claims 1 to 59 and 61 to 84, further comprising the step of training the machine learning algorithm with the values ​​of the plurality of labels and the respective values ​​of the at least one isotopic feature for the plurality of reference samples, prior to step (a).

86. The method according to claim 84 or claim 85, further comprising the step of preprocessing the values ​​of each of the at least one isotope feature before the step of training the machine learning algorithm.

87. The method according to claim 86, wherein the preprocessing step includes controlled imputation of missing values ​​or replacement of missing values ​​with zeros.

88. The method according to claim 86 or claim 87, wherein the preprocessing step includes data normalization.

89. The method according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-88, wherein if the predictive model indicates the presence of cancer, cancer is detected in the subject.

90. The method according to any one of claims 1 to 89, wherein the accuracy rate of cancer prediction by the method is greater than 50%, greater than 60%, greater than 70%, greater than 80%, greater than 90%, or greater than 95%.

91. The method according to any one of claims 1 to 90, wherein the accuracy rate of cancer prediction by the method is approximately 70% or more.

92. The method according to any one of claims 1 to 91, wherein the accuracy of cancer prediction is greater than 90%, for example, greater than 91%, greater than 92%, greater than 93%, greater than 94%, or greater than 95%.

93. The method according to any one of claims 89 to 92, further comprising verification of detected cancer by a method selected from the group consisting of blood tests, urine tests, biopsies, endoscopies, lumbar punctures, Pap tests, surgeries, genetic tests, and imaging.

94. A method for diagnosing cancer in the subject, wherein if the predictive model indicates the presence of cancer, the subject is diagnosed with cancer, according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-93.

95. The method according to any one of claims 1 to 94, wherein the subject has one or more symptoms that indicate the presence of cancer in the subject.

96. A method for prophylactic screening for cancer in the subject, according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-94.

97. The method according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-96, wherein if the subject is diagnosed with cancer, the subject receives treatment for the cancer.

98. The method according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-97, wherein if the subject is diagnosed with cancer, the method further comprises the step of treating the subject for the cancer.

99. Methods for treating cancer in the subject, including the following: (a) A step of selecting a subject diagnosed with cancer according to any one of claims 1, 2, 5-10, 13-20, 23-81, and 84-98, wherein the presence of the cancer is indicated by a predictive model; and (b) The step of treating the subject for the cancer using a treatment for the cancer.

100. A method for monitoring cancer treatment in the subject, according to any one of claims 1 to 93 and 95.

101. The method according to any one of claims 1 to 93, 95, and 100, wherein the subject has previously been diagnosed with cancer and is receiving treatment, or is believed to have previously been diagnosed with cancer and is in remission.

102. The method according to claim 100 or claim 101, wherein if cancer is detected in the subject, cancer treatment for the subject is continued or resumed.

103. The method according to any one of claims 100 to 102, further comprising the step of continuing or resuming treatment of the subject for cancer if cancer is detected in the subject.

104. Methods for treating cancer in the subject, including the following: (a) A step of selecting a subject for which cancer is to be detected according to any one of claims 1, 2, 5-10, 13-20, 23-81, 84-93, 95, and 100-103, wherein the presence of the cancer is indicated by a predictive model, and the subject is believed to have been previously diagnosed with the cancer and treated for the cancer, or to have been previously diagnosed with the cancer and is in remission; and (b) The step of treating the subject for the cancer using a treatment for the cancer.

105. The method according to any one of claims 97 to 104, wherein the treatment includes chemotherapy, radiotherapy, cryotherapy, photodynamic therapy, laser therapy, immunotherapy, targeted therapy, hormone therapy, surgical procedures to remove cancer, stem cell transplantation, bone marrow transplantation, or any combination of the foregoing.

106. The method according to any one of claims 1 to 105, wherein the predictive model is constructed and applied using a computer program product which is materially embodied in a non-temporary machine-readable storage medium, including instructions configured to cause one or more data processors to construct and apply the predictive model.

107. (a) A step of determining the value of each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference objects, wherein the value of each of the at least one isotopic feature is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the reference samples among the plurality of reference samples; and (b) A step of applying the values ​​of each of the at least one isotopic features for a plurality of reference samples as input to train a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to detect whether a subject has cancer, including [specific example].

108. (a) For multiple reference samples derived from multiple reference subjects, the step of determining the value of each of at least one isotopic feature, (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) a normalized value of any of (1) to (3) measured in one of the multiple reference samples; and (b) A step of applying the values ​​of each of the at least one isotopic features for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to assess the characteristics of cancer in a given subject, including [specific examples of the subject].

109. The method according to claim 108, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

110. The method according to any one of claims 107 to 109, wherein the reference sample is a solid sample, and the step of determining the value of each of the at least one isotopic feature includes mass spectrometry analysis of the solid reference sample, which is optionally analyzed via combustion, and / or of a liquid sample prepared by the digestion of one or more of the solid reference samples.

111. The step of determining the value of each of the at least one isotopic features, where the reference sample is a solid sample. (i) A step of preparing one or more liquid samples from the solid reference sample for analysis by mass spectrometry, wherein the liquid samples are prepared by digestion of the one or more solid reference samples; (ii) Introducing the prepared liquid sample into a mass spectrometer to detect one or more ionization products of elements and isotopes present in the prepared liquid sample; and (iii) The step of analyzing the element and the detected one or more ionization products of the isotopes in order to create one or more values ​​of the at least one isotopic feature for the prepared liquid sample. The method according to any one of claims 107 to 110, including the method described in any one of claims 107 to 110.

112. The step of determining the value of each of the at least one isotopic features, where the reference sample is a solid sample. (i) Prepare one or more solid samples from the solid reference sample for analysis by mass spectrometry; (ii) Introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of elements and isotopes present in the prepared solid sample, wherein the detection optionally includes combustion of the prepared solid sample; and (iii) Analyzing the elements and the detected one or more ionized products of the isotopes in order to create one or more values ​​of the at least one isotopic feature for the prepared solid sample. The method according to any one of claims 107 to 111, including the method described in any one of claims 107 to 111.

113. The method according to any one of claims 107 to 112, wherein the determination and / or training is performed by a processor of a computing device.

114. (a) A step of receiving values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference objects in one or more processors, wherein each value of the at least one isotopic feature is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the plurality of reference samples; and (b) A step of applying the values ​​of each of the at least one isotope feature for the plurality of reference samples as input for training a predictive model configured to predict the presence or absence of cancer in a subject using at least one processor, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to detect whether a subject has cancer, including [specific example].

115. (a) A step of receiving values ​​for each of at least one isotopic feature for a plurality of reference samples derived from a plurality of reference objects in one or more processors, wherein each value of the at least one isotopic feature is a normalized value of (1) the concentration of an element, (2) the concentration of an isotope, (3) the concentration of an isotope of the isotope, or (4) any of (1) to (3) measured in one of the plurality of reference samples; and (b) A step of applying the values ​​of each of the at least one isotopic features for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject using at least one processor, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to assess the characteristics of cancer in a given subject, including [specific examples of the subject].

116. The method according to claim 115, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

117. The method according to any one of claims 114 to 116, wherein the value of each of the at least one isotopic feature is obtained by mass spectrometry analysis of a liquid sample prepared by digestion of one or more of the reference samples, and / or by mass spectrometry analysis of one or more solid samples of the reference samples, optionally analyzed via combustion.

118. The reference sample is a solid sample, and the method includes a step of determining the value of each of the at least one isotopic feature prior to step (a), and the determining step is (i) Preparing one or more liquid samples from the solid reference sample for analysis by mass spectrometry, wherein the liquid samples are prepared by digestion of the one or more solid reference samples; (ii) Introducing the prepared liquid sample into a mass spectrometer to detect one or more ionization products of elements and isotopes present in the prepared liquid sample; and (iii) Analyzing the elements and the detected ionization products of the isotopes in order to create one or more values ​​of the at least one isotopic feature for the prepared liquid sample. The method according to any one of claims 114 to 117, including the method described in any one of claims 114 to 117.

119. The reference sample is a solid sample, and the method includes, prior to step (a), a step of determining the value of each of the at least one isotopic feature, (i) Prepare one or more solid samples from the solid reference sample for analysis by mass spectrometry; (ii) Introducing the prepared solid sample into a mass spectrometer to detect one or more ionized products of elements and isotopes present in the prepared solid sample, wherein the detection optionally includes combustion of the prepared solid sample; and (iii) Analyzing the elements and the detected one or more ionized products of the isotopes in order to create one or more values ​​of the at least one isotopic feature for the prepared solid sample. The method according to any one of claims 114 to 118, including the method described in any one of claims 114 to 118.

120. The method according to any one of claims 111 to 113, 118, and 119, wherein the analysis of the detected one or more ionization products of an isotope is accompanied by a reference to one or more ionization products of an element present in a standard material comprising a plurality of calibration standard materials and a blank standard material.

121. The method according to any one of claims 111 to 113 and 118 to 120, wherein the analysis of one or more ionized products of an element and an isotope is accompanied by a reference to one or more ionized products of an element and an isotope present in a standard material including an internal standard and / or a reference material.

122. The method according to claim 120 or claim 121, wherein the standard substance is further introduced into the mass spectrometer, and the mass spectrometer detects one or more ionized products of elements and isotopes present in the standard substance and the prepared liquid or solid sample.

123. (a) The step of preparing multiple solid reference samples obtained from multiple reference objects; (b) A step of preparing liquid and / or solid samples from a plurality of solid reference samples for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of one or more of the solid reference samples; (c) The step of preparing a standard material including multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) The step of introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately in order to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) A step of analyzing isotopes and one or more ionized products of the elements detected in order to create a value for each of the plurality of reference samples for at least one isotopic feature, wherein the value for each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope, or (4) a normalized value of any of (1) to (3); (f) a step of receiving the value of each of the at least one isotope feature in one or more processors; and (g) A step of applying the values ​​of each of the at least one isotopic features for the plurality of reference samples as input for training a predictive model configured to predict the presence or absence of cancer in a subject using at least one processor, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to detect whether a subject has cancer, including [specific example].

124. (a) The step of preparing multiple solid reference samples obtained from multiple reference objects; (b) A step of preparing liquid and / or solid samples from a plurality of solid reference samples for analysis by mass spectrometry, wherein the liquid sample is prepared by digestion of one or more of the solid reference samples; (c) The step of preparing a standard material including multiple calibration standards, blank standards, internal standards, and / or reference materials; (d) The step of introducing the standard material and the prepared liquid or solid sample into a mass spectrometer separately in order to detect one or more ionization products of elements and isotopes present in the standard material and the prepared liquid or solid sample, respectively; (e) A step of analyzing one or more ionized products of an element and an isotope that have been detected in order to create a value for each of the plurality of reference samples of at least one isotopic feature, wherein the value for each of the at least one isotopic feature is (1) the concentration of the element, (2) the concentration of the isotope, (3) the concentration of the isotope of the isotope, or (4) a normalized value of any of (1) to (3); (f) a step of receiving the value of each of the at least one isotope feature in one or more processors; and (g) A step of applying the values ​​of each of the at least one isotopic features for the plurality of reference samples as input for training a predictive model configured to predict cancer characteristics in a subject using at least one processor, wherein the predictive model includes at least one machine learning algorithm. A method for training a predictive model to assess the characteristics of cancer in a given subject, including [specific examples of the subject].

125. The method according to claim 124, wherein the characteristic of the cancer is the tumor mass level of the cancer in the subject.

126. The method according to any one of claims 107 to 125, wherein each of the plurality of reference samples comprises keratinous tissue, optionally hard keratinous tissue.

127. The method according to any one of claims 107 to 126, wherein the plurality of reference samples are non-invasive samples, optionally hair samples and / or nail samples.

128. The method according to any one of claims 110 to 113 and 117 to 127, wherein the digestion is by acid digestion and / or thermal digestion.

129. The method according to any one of claims 110 to 113 and 117 to 128, wherein the digestion is by acid digestion and thermal digestion.

130. The aforementioned acid digestion is nitric acid (HNO 3 The method according to claim 128 or claim 129, wherein the method uses ).

131. The aforementioned acid digestion is nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 The method according to any one of claims 128 to 130, wherein the method uses the following:

132. The method according to any one of claims 128 to 131, wherein the thermal digestion is performed by microwaves.

133. The method according to any one of claims 110 to 113 and 117 to 132, wherein the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS).

134. The method according to claim 133, wherein the ICP-MS is performed using kinetic energy discrimination (KED).

135. The method according to any one of claims 107 to 134, wherein the value of each of the at least one isotope feature is a normalized value of any one of (1) to (3).

136. The method according to any one of claims 107 to 135, wherein each of the values ​​of the at least one isotopic feature is a normalized value of the concentration of the isotope.

137. The method according to any one of claims 107 to 135, wherein each of the values ​​of the at least one isotopic feature is a normalized value of the concentration of the isotope of the isotope element.

138. The method according to any one of claims 107 to 137, wherein the normalized value for each of the at least one isotopic feature is an atomic percentage (atomic%), abundance (F), isotope ratio (R), concentration ratio (CR), or delta value (delta), or any combination thereof.

139. The method according to any one of claims 107 to 138, wherein the normalized value of each of the at least one isotopic features is the abundance (F).

140. The method according to any one of claims 107 to 138, wherein the normalized value of each of the at least one isotope feature is the delta value.

141. The method according to claim 138 or claim 140, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

142. The method according to any one of claims 107 to 141, wherein the at least one isotope feature is one isotope feature.

143. The isotope of the at least one isotope feature is 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 Cr, and the method according to any one of claims 107 to 142.

144. The method according to any one of claims 107 to 141, wherein the at least one isotope feature is a plurality of isotope features.

145. The method according to claim 144, wherein the plurality of isotopic features are 2 to 100 isotopic features.

146. The method according to claim 144 or claim 145, wherein the plurality of isotope features comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 or more isotope features.

147. The method according to claim 144 or claim 145, wherein the at least one isotopic feature is a plurality of isotopic features determined from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 or more elements or isotopes.

148. The method according to any one of claims 107 to 147, wherein the element or isotope of the at least one isotopic feature includes Li, B, Na, Mg, Al, P, K, Sc, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ru, Ag, Cd, In, Sn, I, Te, Ba, Hg, Pb, U, C, N, O, or S, or any combination of the foregoing.

149. The method according to any one of claims 107 to 148, wherein the element or isotope of the at least one isotopic feature includes Ag, Al, B, Ba, Br, Ca, Co, Cr, Cu, Fe, Ge, Hg, I, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Ru, Se, Sn, Sr, Te, Ti, U, V, Y, Zn, C, N, O, or S, or any combination of the foregoing.

150. The method according to any one of claims 107 to 149, wherein the element or isotope of the at least one isotopic feature includes Li, B, Mg, Ca, Ti, Cr, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Mo, Ag, Sn, Ba, Hg, Pb, U, C, N, S, or any combination of the aforementioned.

151. The isotope of the at least one isotope feature is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, <> 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The method according to any one of claims 107 to 150, comprising U, or any combination thereof.

152. The method according to any one of claims 107 to 151, wherein the isotope of at least one isotopic feature comprises Zn.

153. The isotope of the at least one isotope feature is 64 Zn and / or 66 A method according to any one of claims 107 to 152, comprising Zn.

154. The method according to any one of claims 107 to 149, wherein the isotope of the at least one isotopic feature comprises N, C, S, or O.

155. The isotope of the at least one isotope feature is 34 The method according to any one of claims 107 to 151 and 154, comprising S.

156. The method according to any one of claims 107 to 155, wherein the machine learning algorithm is a supervised machine learning algorithm.

157. The method according to any one of claims 107, 108, 110-115, 117-124, and 126-156, wherein the prediction model is a classification model.

158. The method according to any one of claims 107, 108, 110-115, 117-124, and 126-157, wherein the prediction model is a binary classification model.

159. The method according to any one of claims 107, 108, 110-115, 117-124, and 126-157, wherein the predictive model is a multi-class classification model.

160. The method according to any one of claims 107, 108, 110-115, 117-124, and 126-159, wherein the machine learning algorithm is an ensemble algorithm comprising logistic regression (e.g., ridge, lasso, or elastic net), naive Bayes classifier, Gaussian NB, Gaussian NB isotonic, Gaussian NB sigmoid, random forest classifier, gradient boosting classifier, XG boost classifier, histogram gradient boosting classifier, deep neural network classifier, decision tree classifier, bagging classifier, extra tree classifier, Ada Boost classifier, Cat Boost classifier, LGBM classifier, support vector classifier, perceptron classifier (e.g., multilayer perceptron classifier), deep learning classifier for tabular data (e.g., TabNet or TabML), passive aggressive classifier, or SGD classifier, or any combination of the foregoing.

161. The method according to any one of claims 107, 108, 110-115, 117-124, and 126-160, wherein the machine learning algorithm is a support vector classifier, a decision tree-based ensemble classifier, or a multilayer perceptron classifier.

162. The method according to any one of claims 108 to 113, 115, and 125 to 156, wherein the predictive model is a regression model.

163. The method according to any one of claims 107 to 155, wherein the machine learning algorithm is an unsupervised machine learning algorithm.

164. The method according to any one of claims 107 to 163, wherein the prediction model is a general model.

165. The method according to claim 164, wherein at least one of the multiple reference subjects lives in a different geographical area, is of a different sex, is of a different age, belongs to a different cultural or ethnic group, has a different diet, and / or has a different type of cancer compared to another of the multiple reference subjects.

166. The method according to claim 165, wherein the plurality of references originate from at least two, three, four, five, six or more different countries.

167. The method according to any one of claims 164 to 166, wherein at least one of the multiple reference subjects is of a different sex from another of the multiple reference subjects.

168. The method according to any one of claims 107 to 163, wherein the prediction model is a specialized model.

169. The method according to claim 168, wherein each of the multiple reference subjects lives in the same geographical area, is of the same sex, is of the same age, and / or has the same type of cancer.

170. The method according to claim 168 or claim 169, wherein each of the multiple reference objects is of the same sex.

171. The method according to any one of claims 168 to 170, wherein each of the multiple reference subjects is male.

172. The method according to any one of claims 168 to 170, wherein each of the multiple reference subjects is female.

173. The method according to any one of claims 168 to 172, wherein each of the multiple reference targets has the same type of cancer.

174. The method according to any one of claims 107 to 173, wherein the plurality of reference objects are mammals.

175. The method according to any one of claims 107 to 174, wherein the plurality of reference subjects are human beings.

176. The method according to any one of claims 107 to 175, wherein the cancer is a blood cancer or a solid tumor.

177. The method according to claim 176, wherein the blood cancer is leukemia, lymphoma, or multiple myeloma.

178. The aforementioned cancers include lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, non-Hodgkin's disease, and Waldenström macrocytosis. Globulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal carcinoma, Wilms' tumor, cervix Cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), epithelial ovarian cancer, ovarian cancer, egg A method according to any one of claims 107 to 177, selected from the group consisting of ductal carcinoma, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary carcinoma, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil carcinoma, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal / stomach (GIST) cancer, lymphoma, squamous cell carcinoma of the head and neck (SCCHN), salivary gland carcinoma, brain cancer, and malignant peripheral nerve sheath tumor (MPNST) associated with neurofibromatosis type 1.

179. The method according to any one of claims 107 to 178, wherein the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia.

180. The method according to any one of claims 107 to 179, wherein some of the aforementioned reference subjects are known to have cancer, and it is assumed that some of the aforementioned reference subjects do not have cancer.

181. The method according to any one of claims 107-162 and 164-180, wherein multiple labels are used to train the machine learning algorithm, and the multiple labels include a label for each of the multiple reference samples.

182. The method according to claim 181, wherein the label indicates whether the reference sample is derived from a reference subject known to have cancer or from a reference subject assumed not to have cancer.

183. The method according to claim 181, wherein the label indicates the characteristics of the reference cancer from which the acquired reference sample originates.

184. The method according to claim 181 or claim 183, wherein the label indicates the tumor load level of the reference cancer from which the acquired reference sample originates.

185. The method according to any one of claims 107 to 184, further comprising preprocessing the values ​​of each of the at least one isotope feature before training the machine learning algorithm.

186. The method according to claim 185, wherein the preprocessing includes comparative comparison and imputation of missing values ​​or replacement of missing values ​​with zeros.

187. The method according to claim 185 or claim 186, wherein the preprocessing includes normalizing the data.

188. The method according to any one of claims 107, 110-114, 117-123, 126-182, and 185-187, wherein if the predictive model indicates the presence of cancer, cancer is detected in the subject.

189. The method according to any one of claims 107 to 188, wherein the predictive model is constructed using a computer program product that is materially embodied in a non-temporary machine-readable storage medium, which includes instructions configured to cause one or more data processors to construct the predictive model.

190. (a) A step in which one or more ionized products of isotopes present in a solid sample of the subject are detected by mass spectrometry, wherein the solid sample is optionally analyzed in the form of a liquid sample prepared by combustion or by digestion of the solid sample, and the one or more ionized products are 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 U, or at least one isotope selected from any combination of the aforementioned; and (b) A step of determining the value of at least one isotope feature, wherein the at least one isotope feature is the concentration of at least one isotope or its normalized value. A method for determining the value of at least one isotopic feature of an isotope in a solid sample derived from a target, including the following.

191. The method according to claim 190, wherein each value of the at least one isotope feature is a normalized value of the concentration of the at least one isotope.

192. The method according to claim 190 or claim 191, further comprising the step of normalizing the value of each of the at least one isotopic feature.

193. The method according to any one of claims 190 to 192, wherein the normalized value of each of the at least one isotopic feature is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination thereof.

194. (a) A step of detecting one or more ionized products of isotopes present in a solid sample of the subject by mass spectrometry, wherein the solid sample is optionally analyzed by combustion or in the form of a liquid sample prepared by digestion of the solid sample, and the one or more ionized products are 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 A stage that is U, or at least one isotope selected from any combination of the aforementioned; (b) A step of determining the value of at least one isotopic feature, wherein the at least one isotopic feature is the concentration of at least one isotope; (c) A step of normalizing a value which is the concentration of the at least one isotope, wherein the normalized value for each of the at least one isotope feature is an atomic percentage (atomic%), abundance (F), isotope ratio (R), or delta value (delta), or any combination of the foregoing. A method for determining the value of at least one isotopic feature of an isotope in a solid sample derived from a target, including the following.

195. The method according to any one of claims 190 to 194, wherein the normalized value of each of the at least one isotopic feature is the abundance (F).

196. The method according to any one of claims 190 to 195, wherein the normalized value of each of the at least one isotope feature is the delta value.

197. The method according to any one of claims 193, 194, and 196, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

198. The method according to any one of claims 190 to 197, wherein the step of determining the value of each of the at least one isotopic feature involves referencing one or more isotopic features present in a standard material including a plurality of calibration standard materials and a blank standard material.

199. The method according to any one of claims 190 to 198, wherein the step of determining the value of each of the at least one isotopic feature involves referencing one or more isotopic features present in a standard material including an internal standard and / or a reference material.

200. The method according to claim 198 or claim 199, further comprising the step of introducing the standard substance into a mass spectrometer, wherein the mass spectrometer detects one or more ionized products of isotopes present in the standard substance and the prepared liquid or solid sample.

201. The method according to any one of claims 190 to 200, wherein the sample comprises keratinous tissue, optionally hard keratinous tissue.

202. The method according to any one of claims 190 to 201, wherein the sample is a non-invasive sample, optionally a hair sample or a nail sample.

203. The method according to any one of claims 190 to 202, wherein the digestion is by acid digestion and / or thermal digestion.

204. The method according to any one of claims 190 to 203, wherein the digestion is by acid digestion and thermal digestion.

205. The aforementioned acid digestion is nitric acid (HNO 3 The method according to claim 203 or claim 204, wherein the method uses ).

206. The aforementioned acid digestion is nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 The method according to any one of claims 203 to 205, wherein the method uses the following:

207. The method according to any one of claims 203 to 206, wherein the thermal digestion is performed by microwaves.

208. The method according to any one of claims 190 to 207, wherein the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS).

209. The method according to claim 208, wherein the ICP-MS is performed using kinetic energy discrimination (KED).

210. The method according to any one of claims 190 to 209, wherein the at least one isotope is one isotope.

211. The aforementioned at least one isotope, 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 The method according to any one of claims 190 to 210, wherein the material is Cr.

212. The aforementioned isotope, 64 Zn, 66 Zn, or 34 The method according to any one of claims 190 to 210, wherein S.

213. The method according to any one of claims 190 to 212, wherein the at least one isotope is a plurality of isotopes.

214. The method according to claim 213, wherein the plurality of isotopes are 2 to 59 isotopes.

215. The method according to claim 213 or claim 214, wherein the at least one isotope is isotope 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59.

216. The method according to any one of claims 190 to 215, wherein the subject is suspected of having cancer.

217. The method according to any one of claims 190 to 216, further comprising the step of selecting a subject suspected of having cancer.

218. The method according to any one of claims 190 to 217, wherein the subject has one or more symptoms indicating that cancer may be present in the subject.

219. The method according to any one of claims 190 to 218, wherein the cancer is a blood cancer or a solid tumor.

220. The method according to claim 219, wherein the blood cancer is leukemia, lymphoma, or multiple myeloma.

221. The aforementioned cancers include lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, non-Hodgkin's disease, and Waldenström macrocytosis. Globulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal carcinoma, Wilms' tumor, cervix Cancer, uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), epithelial ovarian cancer, ovarian cancer, egg The method according to any one of claims 216 to 220, selected from the group consisting of ductal carcinoma, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary carcinoma, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil carcinoma, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal / stomach (GIST) cancer, lymphoma, squamous cell carcinoma of the head and neck (SCCHN), salivary gland carcinoma, brain cancer, and malignant peripheral nerve sheath tumor (MPNST) associated with neurofibromatosis type 1.

222. The method according to any one of claims 216 to 221, wherein the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia.

223. The method according to any one of claims 190 to 222, wherein the subject is a mammal.

224. The method according to any one of claims 190 to 223, wherein the subject is a human.

225. A non-invasive method according to any one of claims 190 to 224.

226. A kit for determining the value of at least one isotopic feature in a sample derived from a target, wherein the at least one isotopic feature is the concentration or normalized value of at least one isotope, 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, 238 U, or any combination of the above, This kit is, (i) A reference material comprising a verified standard material for at least one isotope; (ii) Packaging materials; and (iii) Instructions for use of the kit, for determining the value of the at least one isotopic feature. The kit is equipped with the aforementioned features.

227. The kit according to claim 226, wherein each of the values ​​of the at least one isotope feature is a normalized value of the concentration of the at least one isotope.

228. The kit according to claim 226 or claim 227, wherein the normalized value for each of the at least one isotopic feature is an atomic percentage (atomic%), abundance (F), isotopic ratio (R), or delta value (delta), or any combination of the foregoing.

229. The kit according to any one of claims 226 to 228, wherein the normalized value of each of the at least one isotopic features is the abundance (F).

230. The kit according to any one of claims 226 to 228, wherein the normalized value of each of the at least one isotope feature is the delta value.

231. The kit according to claim 228 or 230, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

232. The kit according to any one of claims 226 to 231, wherein the value of each of the at least one isotopic feature is associated with a reference to one or more isotopic features present in the reference material.

233. A kit according to any one of claims 226 to 232, further comprising a calibration standard, a blank standard, and / or an internal standard.

234. The kit according to any one of claims 226 to 233, wherein the sample comprises keratinous tissue, optionally hard keratinous tissue.

235. The kit according to any one of claims 226 to 234, wherein the sample is a non-invasive sample, optionally a hair sample or a nail sample.

236. The kit according to any one of claims 226 to 235, wherein the sample is a digested sample.

237. The kit according to any one of claims 226 to 236, comprising instructions for use for digesting the aforementioned sample.

238. The kit according to claim 236 or claim 237, wherein the digestion is by acid digestion and / or thermal digestion.

239. A kit according to any one of claims 236 to 238, wherein digestion is performed by acid digestion and thermal digestion.

240. The aforementioned acid digestion is nitric acid (HNO 3 The kit according to claim 238 or claim 239, which uses ).

241. The aforementioned acid digestion is nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 A kit according to any one of claims 238 to 240, which uses )

242. The kit according to any one of claims 238 to 241, wherein the thermal digestion is performed by microwaves.

243. The kit according to any one of claims 226 to 242, wherein the at least one isotope is one isotope.

244. The aforementioned at least one isotope, 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 A kit according to any one of claims 226 to 242, wherein the material is Cr.

245. The aforementioned at least one isotope, 64 Zn, 66 Zn, or 34 A kit according to any one of claims 226 to 243, wherein S.

246. The kit according to any one of claims 226 to 245, wherein the at least one isotope is a plurality of isotopes.

247. The kit according to claim 246, wherein the aforementioned multiple isotopes comprise 2 to 59 isotopic features.

248. The kit according to claim 246 or claim 247, wherein the plurality of isotopes comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59 isotopic features.

249. The aforementioned multiple types of isotopes, 64 Zn, 66 Zn, and / or 34 A kit according to any one of claims 246 to 248, wherein the concentration of S is [amount].

250. The kit according to any one of claims 226 to 249, wherein the subject is suspected of having cancer.

251. The kit according to any one of claims 226 to 250, wherein the subject has one or more symptoms that indicate the presence of cancer in the subject.

252. The kit according to claim 250 or claim 251, wherein the cancer is a blood cancer or a solid tumor.

253. The kit according to claim 252, wherein the blood cancer is leukemia, lymphoma, or multiple myeloma.

254. The aforementioned cancers include lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, non-Hodgkin's disease, and Waldenström macroglossitis. Robulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal carcinoma, Wilms' tumor, cervical cancer Uterine cancer, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial cancer, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube A kit according to any one of claims 250 to 253, selected from the group consisting of cancer, papillary serous cystadenocarcinoma, papillary serous carcinoma of the uterus (UPSC), hepatobiliary carcinoma, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic thyroid carcinoma, tonsil carcinoma, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal / stomach (GIST) cancer, lymphoma, head and neck squamous cell carcinoma (SCCHN), salivary gland carcinoma, brain cancer, and malignant peripheral nerve sheath tumor (MPNST) associated with neurofibromatosis type 1.

255. The kit according to any one of claims 250 to 254, wherein the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia.

256. The kit according to any one of claims 226 to 255, wherein the subject is a mammal.

257. The kit according to any one of claims 226 to 256, wherein the subject is a human.

258. A method for determining the value of at least one isotopic feature derived from a sample of interest, comprising the step of using a kit according to any one of claims 226 to 257 in accordance with the instructions for use.

259. An isotope profile identified by mass spectrometry, wherein the isotope profile originates from a solid sample of the subject, and the isotope profile is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The isotope profile comprising the value of at least one isotope feature of at least one isotope selected from U or any combination of the foregoing, wherein the at least one isotope feature is the concentration of the at least one isotope.

260. An isotope profile identified by mass spectrometry, wherein the isotope profile originates from a solid sample of the subject, and the isotope profile is 13 C, 34 S, 10 B, 11 B, 24 Mg, 25 Mg, 26 Mg, 43 Ca, 44 Ca, 46 Ti, 47 Ti, 48 Ti, 49 Ti, 50 Cr, 52 Cr, 53 Cr, 56 Fe, 57 Fe, 58 Ni, 60 Ni, 61 Ni, 62 Ni, 63 Cu, 65 Cu, 64 Zn, 66 Zn, 67 Zn, 68 Zn, 77 Se, 78 Se, 82 Se, 79 Br, 81 Br, 85 Rb, 87 Rb, 84 Sr, 86 Sr, 87 Sr, 88 Sr, 107 Ag, 109 Ag, 116 Sn, 117 Sn, 118 Sn, 119 Sn, 120 Sn, 135 Ba, 136 Ba, 137 Ba, 138 Ba, 199 Hg, 200 Hg, 201 Hg, 202 Hg, 206 Pb, 207 Pb, 208 Pb, 235 U, or 238 The isotope profile comprising the value of at least one isotope feature of at least one isotope selected from U or any combination of the foregoing, wherein the at least one isotope feature is the concentration of the at least one isotope, or a normalized value of the concentration of the at least one isotope.

261. The isotope profile according to claim 260, wherein the value of the at least one isotope feature is a normalized value of the concentration of the at least one isotope.

262. The isotope profile according to claim 260 or claim 261, wherein the normalized value is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination thereof.

263. The isotope profile according to any one of claims 260 to 262, wherein the normalized value is the abundance ratio (F).

264. The isotope profile according to any one of claims 260 to 262, wherein the normalized value is the delta value.

265. The isotope profile according to claim 262 or claim 264, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

266. The isotopic profile according to any one of claims 259 to 265, wherein the solid sample comprises keratinous tissue, optionally hard keratinous tissue.

267. The isotope profile according to any one of claims 259 to 266, wherein the solid sample is a non-invasive sample, optionally a hair sample or a nail sample.

268. The isotope profile according to any one of claims 259 to 267, wherein the solid sample is digested into a liquid sample prepared for mass spectrometry.

269. The isotopic profile according to claim 268, wherein the digestion is by acid digestion and / or thermal digestion.

270. The isotope profile according to claim 268 or claim 269, wherein the digestion is by acid digestion and thermal digestion.

271. The aforementioned acid digestion is nitric acid (HNO 3 The isotope profile according to claim 269 or claim 270, which uses ).

272. The aforementioned acid digestion is nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 The isotope profile according to any one of claims 269 to 271, wherein the following is used.

273. The isotope profile according to any one of claims 269 to 272, wherein the thermal digestion is performed by microwaves.

274. The isotope profile according to any one of claims 259 to 273, wherein the mass spectrometry method is inductively coupled plasma mass spectrometry (ICP-MS).

275. The isotope profile according to claim 274, wherein the ICP-MS is performed using kinetic energy discrimination (KED).

276. The isotope profile according to any one of claims 259 to 275, wherein the at least one isotope is a single isotope.

277. The aforementioned at least one isotope, 66 Zn, 64 Zn, 68 Zn, 87 Sr, 88 Sr, 86 Sr, 47 Ti, 48 Ti, 46 Ti, and / or 50 The isotope profile of Cr according to any one of claims 259 to 276.

278. The at least one isotope 64 Zn, 66 Zn, or 34 The isotope profile according to any one of claims 259 to 276, wherein the isotope is S.

279. The isotope profile according to any one of claims 259 to 275, wherein the at least one isotope is a plurality of isotopes.

280. The isotope profile according to claim 279, wherein the aforementioned multiple isotopes are 2 to 59 isotopes.

281. The isotope profile according to claim 279 or claim 280, wherein the plurality of isotopes comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59 isotopes.

282. The aforementioned multiple types of isotopes, 64 Zn, 66 Zn, and / or 34 An isotope profile according to any one of claims 279 to 281, including the concentration of S.

283. The isotope profile according to any one of claims 259 to 282, wherein the subject is suspected of having cancer.

284. The isotope profile according to any one of claims 259 to 283, wherein the subject has one or more symptoms indicating that cancer may be present in the subject.

285. The isotopic profile according to claim 283 or claim 284, wherein the cancer is a blood cancer or a solid tumor.

286. The isotope profile according to claim 285, wherein the blood cancer is leukemia, lymphoma, or multiple myeloma.

287. The aforementioned cancers include lung cancer, thyroid cancer, ovarian cancer, colorectal cancer, prostate cancer, pancreatic cancer, esophageal cancer, liver cancer, breast cancer, skin cancer, mesothelioma, acute leukemia, acute lymphoblastic leukemia, acute myeloid leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, acute erythroleukemia, chronic leukemia, chronic myeloid leukemia, chronic lymphocytic leukemia, polycythemia vera, Hodgkin's disease, non-Hodgkin's disease, and Waldenström macroglomerulonephrosis. Bulinemia, multiple myeloma, heavy chain disease, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endosarcoma, lymphangiosarcoma, lymphangiosarcoma, synoviomas, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal carcinoma, Wilms' tumor, cervical cancer, child Cancer of the uterus, testicular cancer, gallbladder cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial carcinoma, glioma, astrocytoma, glioblastoma multiforme, medulloblastoma, craniopharyngioma, ependymoma, pineal glandoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, schwannoma, neurofibrosarcoma, meningioma, melanoma, neuroblastoma, retinoblastoma, kidney cancer, hepatocellular carcinoma (HCC), hepatoblastoma, colorectal cancer, colon cancer, rectal cancer, anal cancer, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), epithelial ovarian cancer, ovarian cancer, fallopian tube cancer, breast cancer An isotope profile according to any one of claims 283 to 286, selected from the group consisting of serous cystadenocarcinoma capitatum, serous carcinoma papillate of the uterus (UPSC), hepatobiliary carcinoma, synovial sarcoma of soft tissue and bone, Ewing's sarcoma, anaplastic carcinoma of the thyroid gland, tonsil carcinoma, adrenocortical carcinoma, pancreatic ductal carcinoma, pancreatic adenocarcinoma, gastrointestinal / stomach (GIST) cancer, lymphoma, squamous cell carcinoma of the head and neck (SCCHN), salivary gland carcinoma, brain cancer, and malignant peripheral nerve sheath tumor (MPNST) associated with neurofibromatosis type 1.

288. The isotope profile according to any one of claims 283 to 287, wherein the cancer is prostate cancer, bladder cancer, kidney cancer, head and neck cancer, breast cancer, or leukemia.

289. The isotope profile according to any one of claims 259 to 288, wherein the subject is a mammal.

290. The isotope profile according to any one of claims 259 to 289, wherein the subject is human.

291. A method for determining whether a subject has cancer, comprising the step of comparing one or more isotopic features from an isotopic profile described in any one of claims 259 to 290 with one or more corresponding isotopic features of (i) an individual characterized as having cancer, (ii) an individual characterized as not having cancer, or (iii) an individual characterized as having cancer and an individual characterized as not having cancer.

292. The method according to claim 291, wherein the comparison step is performed by inputting the values ​​of one or more isotopic features derived from an isotopic profile into a predictive model configured to predict the presence or absence of cancer in a subject, wherein the predictive model includes at least one machine learning algorithm trained with the values ​​of one or more corresponding isotopic features derived from a plurality of reference samples derived from a plurality of reference subjects.

293. The method according to claim 292, wherein the predictive model determines whether the subject has cancer.

294. The method according to claim 292 or claim 293, wherein the plurality of reference subjects include (i) individuals characterized as having cancer, (ii) individuals characterized as not having cancer, or (iii) both individuals characterized as having cancer and individuals characterized as not having cancer.

295. The method according to any one of claims 291 to 294, wherein the isotope profile is received as a data file electronically communicated from a physician, laboratory, or other service provider.

296. The method according to any one of claims 291 to 295, wherein the determination of whether the subject has cancer is communicated electronically to a physician, laboratory, or other service provider, and optionally such electronic communication includes (i) comparing the isotopic features of the subject's isotopic profile with corresponding isotopic features from multiple reference samples from multiple reference subjects, (ii) determining the probability that the subject has cancer, or (iii) both of (i) and (ii).

297. The method according to any one of claims 291 to 296, wherein the isotope profile includes at least one normalized value for the concentration of at least one isotope of the isotope profile.

298. The method according to claim 297, wherein the normalized value is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), or a delta value (delta), or any combination thereof.

299. The method according to claim 297 or claim 298, wherein the normalized value is the abundance ratio (F).

300. The method according to claim 297 or claim 298, wherein the normalized value is a delta value.

301. The method according to claim 300, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

302. A method for determining whether a subject has cancer, (a) A step of determining a normalized value of the concentration of at least one isotope in the isotope profile according to any one of claims 259 to 290, (b) The step of determining whether the subject has cancer based on the normalized value. The method, including the method described above.

303. The method according to claim 302, wherein the normalized value is an atomic percentage (atomic%), an abundance (F), an isotopic ratio (R), a delta value (delta), or any combination thereof.

304. The method according to claim 302 or claim 303, wherein the normalized value is the abundance ratio (F).

305. The method according to claim 302 or claim 303, wherein the normalized value is a delta value.

306. The method according to claim 303 or claim 305, wherein the delta value is a conventional delta value, a study-specific delta value (InH), a dissimilar simple nitrogen-based delta value (simple Nδ), or a dissimilar pseudo-nitrogen-based delta value (pseudo-Nδ).

307. (a) Selecting a subject for which cancer is detected according to any one of claims 291 to 306; and (b) Treating the subject for the cancer using a treatment for the cancer. A method for treating cancer in a subject, including [specific example].

308. The method according to claim 307, wherein the treatment includes chemotherapy, radiotherapy, cryotherapy, photodynamic therapy, laser therapy, immunotherapy, targeted therapy, hormone therapy, surgical procedures to remove cancer, stem cell transplantation, bone marrow transplantation, or any combination of the foregoing.

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