Method for diagnosing or monitoring disease in subject using spectroscopy

Abstract

Methods of using spectral data to diagnose a disease, such as breast cancer, in a biological sample are described. The method relates to a computer-implemented method that converts spectral vibrations of a sample into a map and scores the map using a set of reference maps. Based on the score and a threshold, it may be determined whether the subject obtaining the sample is diseased, and if the subject is diseased, the degree of diseased is determined. The methods may also detect early and pre-illness states of a subject by detecting a low concentration analyte signal indicative of the early or pre-illness state. The method is non-invasive, non-subjective, highly specific and sensitive. The method provides an application of a single standard of diagnostic accuracy, independent of whether there is a pathology expert.

Description

Technical Field

[0001] This invention generally relates to the assessment or monitoring of diseases of a subject, and more specifically to the use of spectral analysis of biological samples of the subject. Background Technology

[0002] Over the past fifty to sixty years, tremendous efforts have been made to improve the ability to diagnose diseases in humans and other animals by analyzing biological samples, and to develop effective treatments to control or eliminate diseases. Furthermore, efforts have been made to develop the ability to track disease progression from the earliest detectable time to determine the optimal timing for treatment intervention and to select the most effective and efficient treatment plan for each patient.

[0003] Despite numerous efforts, existing biosample assessment methods still lack large-scale application in clinical settings and the ability to monitor the impact of treatment regimens on the progression of diseases such as cancer in real time and in a controlled manner. Therefore, whether in the early stages (e.g., precancerous) or late stages, there is a lack of clinically applicable solutions to accurately compare the characteristics of samples collected at different times / stages to assess the rate of disease progression or regression in each patient in real time, triggering controlled measures that can be implemented at a reasonable total cost.

[0004] Current clinical methods cannot accurately assess the progression of precancerous lesions in individual patients. The course of the disease and its response to treatment can only be understood through retrospective epidemiological studies, which at best provide the average course of the disease and the average response to treatment.

[0005] Another problem with current diagnostic methods involves subjectivity, often manifested as poor consistency in the findings of different pathologists examining the same dataset. This subjectivity is particularly pronounced in diagnostic methods involving microscopic examination of samples. Complicating diagnosis further, even if all cells in a tissue section or slide are affected, the extent of their impact is not uniform. Furthermore, extensive changes in the chemical and physical properties of cells at the molecular level may not be apparent due to morphological changes in cells during the pre-analytical pathological phase. Moreover, especially in early diagnoses, pathologists often search for a few diseased cells among a large number of seemingly normal cells. Therefore, the validity of a diagnosis is linked to the pathologist's skill, diligence, and prior experience. As long as the fundamental approach to diagnostic pathology remains a subjective process, it is difficult to control for these discrepancies.

[0006] In addition, many existing methods for obtaining samples involve invasive and sometimes cumbersome procedures, including but not limited to fine-needle aspiration, large biopsy, sentinel lymph node dissection, and sometimes prior breast surgery.

[0007] The goal of all new cancer diagnostic methods is to develop a test that can detect disease at a very early stage, characterized by being less invasive, less costly, and more specific. Currently available cancer diagnostic tests can be categorized into four types: cytology-based tests, which provide cellular evidence of abnormal cancer cells; imaging tests, which are scan-based tests that provide visual evidence of the disease (e.g., ultrasound for ovarian cancer, Basic Specific Gamma Imaging (BSGI) for breast cancer); biological tests that measure disease-related biological factors (e.g., the HER-2 test for breast cancer); and genomic tests that display and measure an individual's genetic susceptibility to cancer, or analyze the activity of a set of genes that can influence how cancer may manifest and its response to treatment (e.g., the Oncotype-DX assay for breast cancer).

[0008] Tumors with similar histopathological appearances may have significantly different clinical courses and show different responses to treatment. In rare cases, this clinical heterogeneity can be explained by classifying morphologically similar tumors into subtypes of different pathogens. For example, breast cancer is classified into 5 different subtypes, colorectal cancer into 7 subtypes, and lung cancer into as many as 20 subtypes, as documented in the scientific literature.

[0009] One advance in cancer diagnosis is the identification of biomarkers. These biomarkers are biological molecules that appear due to the development of disease. Sometimes, elevated levels of these molecules are found, which may be associated with cancer.

[0010] Another advancement in improving breast cancer diagnostic assessment is immunohistochemistry (IHC). IHC involves using specific antibodies to detect the expression and levels of known biomarkers in a biological sample. However, IHC has not yet fully met the needs of the diagnostic industry because it requires accurate labeling, tagging, and specific knowledge of the molecular biomarkers expressed in the disease, and because antibody preparation for these biomarkers is cumbersome.

[0011] In cancer pathology laboratories, quality can also be an issue. The consequences of false detections can range from minor (assigning a normal result to an unrelated healthy individual) to serious (implementing incompatible treatments). Generally, errors related to clinical laboratory testing fall into three categories: pre-analytical errors, analytical errors, and post-analytical errors.

[0012] Pre-analytical errors occur before sample testing. The vast majority of these errors occur even before the sample arrives at the laboratory and may not involve laboratory personnel. Pre-analytical errors include incorrect patient identification, inadequate patient preparation, collection of inappropriate samples, collection of appropriate samples in inappropriate containers, and sample degradation due to improper handling (e.g., overheating, freezing, or transport delays). Pre-analytical errors are among the most common errors associated with clinical laboratory testing. One reason for these errors is that the steps involved in sample collection have not been standardized or uniformly accepted by all personnel involved in the process (e.g., physicians, nurses, medical interns, patient care technicians, or clerical staff). The scarcity and quality of these resources are some other possible causes of pre-analytical errors.

[0013] Analytical errors can occur after the laboratory receives the sample, and sometimes during the analytical phase of the test. Because most tests in today's clinical laboratories are automated, they account for a relatively small percentage of errors. Errors during the analytical phase can be attributed to laboratory personnel and typically include mistakes in specimen handling and storage, undetected quality control failures, instrument calibration errors, instrument misuse, procedural biases and errors, interfering substances, and the use of inappropriate or expired reagents. Laboratories can control the analytical phase of testing by strictly adhering to standard operating procedures, focusing on quality control results, performing regular preventative instrument maintenance, and conducting periodic proficiency tests. Although analytical errors are relatively rare, they can be frustrating, often only being discovered after the laboratory has been informed that the final results are inconsistent with the patient's condition, status, or treatment.

[0014] Post-analysis errors refer to errors that occur during the reporting and / or interpretation of test results. These errors are typically caused by laboratory personnel or those who placed the test orders. Post-analysis errors may occur more frequently than analytical errors, but less frequently than pre-analysis errors. Failure to publish results or result entry errors (especially for manually recorded tests) can lead to misallocation, misuse of tests, and excessive turnaround times.

[0015] The following section details other attempted solutions involving the analysis of biological samples using optical spectroscopy techniques to improve cancer assessment.

[0016] The diagnostic pathology services used to perform these analyses are inherently limited. For example, these methods often require understanding of pre-existing correlations between peaks, peak shapes, various wavelengths, and specific organs to make a meaningful diagnosis. Furthermore, the need to understand the different vibrational modes of biomolecules can further complicate the analysis. Therefore, these methods typically require specialized pathologists to interpret and / or analyze the spectra, which in turn makes it difficult to provide high-quality diagnostic pathology services in areas of the world with limited access to healthcare. Consequently, these services may not be available if there is a lack of trained pathologists near the biosample collection site. Thus, the quality of these services can be very poor without the assistance of trained pathologists.

[0017] Another limitation is the poor specificity and sensitivity of spectroscopic techniques. These techniques detect substructures present in molecules but may not be able to detect the entire molecule. Furthermore, the presence of the same substructure in different molecules can lead to overlapping spectral responses, thus limiting the identification of specific molecules in complex samples. Other spectroscopic techniques are generally limited by their inability to reliably detect the presence of components below 5% by weight in the total mass of a sample.

[0018] Therefore, there remains an unmet need to develop non-invasive, non-subjective, highly specific and / or sensitive methods for screening, diagnosing and / or predicting diseases in humans or animals. Summary of the Invention

[0019] This article discloses a non-invasive, non-subjective quantitative system and method for analyzing biological samples and interpreting the presence or absence of disease and, optionally (if present), the stage or grade level of disease.

[0020] A method is described for breast cancer screening, diagnosis, and / or prognosis in subjects using molecular biomarkers in subject samples. The subjects can be humans or other animals, and the method can be performed, for example, in vitro. The method combines optical spectroscopy techniques and computer-based implementation techniques.

[0021] For example:

[0022] Optical spectroscopy was performed using a Raman spectrometer with a 785 nm wavelength laser, and measurements were taken, for example, at 3100 cm⁻¹. -1 Up to 900cm -1 Between, for example, 3050.855cm -1 Up to 929.527cm -1 The vibration frequency range (including endpoint values) and 1101 points. The number of points and the vibration frequency range are determined by the selected spectral frequency range and the spectral resolution of the instrument.

[0023] This article also describes methods for using the methods described herein. These methods can be used for the screening, diagnosis, and / or prognosis of breast cancer.

[0024] In one aspect of the present invention, a method for diagnosing or monitoring a disease of a subject is provided, the method comprising:

[0025] 1) Provide or obtain samples from the object;

[0026] 2) Generate the spectral spectrum of the sample;

[0027] 3) Compare the spectrum with one or more reference spectra to obtain an overall score;

[0028] 4) Determine the disease status by comparing the overall score with a threshold.

[0029] In another aspect of one or more of the above methods, generating a spectral spectrum includes:

[0030] Obtain the original vector Vr, which is a two-dimensional vector of multiple wavenumbers, where each intensity value corresponds to a wavenumber;

[0031] Filtering the original vector; and

[0032] The multiple intensity values ​​of the original vector are normalized to generate a normalized vector Vn.

[0033] In another aspect of one or more of the methods described above, filtering includes smoothing the original vector and removing background noise.

[0034] In another aspect of one or more of the above methods, a spectral map is further generated by selecting a subset vector (Vf) of the normalized vector Vn.

[0035] In another aspect of one or more of the methods described above, the subset vector may include, for example, 6 to 30 components.

[0036] In another aspect of the above one or more methods, the wavenumber of the subset vector is at a width of 20cm. -1 Up to 60cm -1 Within the non-overlapping range between them.

[0037] In another aspect of one or more of the above methods, the overall score is determined by a polynomial model of the subset vectors from order 1 to 6, with coefficients determined on the learning set.

[0038] In another aspect of one or more of the above methods, the overall score is compared with a threshold.

[0039] In another aspect of one or more of the above methods, the threshold is adjusted to a higher value for diagnosing the disease, or adjusted to a lower value for monitoring the disease.

[0040] In another aspect of one or more of the above methods, Vf includes approximately 300 cm. -1 Approximately 1900cm -1 One or more values ​​within a range.

[0041] In another aspect of one or more of the above methods, Vf includes approximately 620 cm. -1 Approximately 670cm -1 The four values ​​between, approximately 720cm -1 Approximately 760cm -1 The two values ​​between, approximately 1550cm -1 Approximately 1580cm -1 The two values ​​between, and approximately 1740cm -1 Approximately 1790cm -1 The four values ​​between.

[0042] In another aspect of one or more of the above methods, the spectral spectrum is generated by vibrational spectroscopy, field-resolved spectroscopy, frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, or a combination thereof.

[0043] In another aspect of one or more of the above methods, field-resolved spectroscopy includes field-resolved infrared spectroscopy.

[0044] In another aspect of one or more of the above methods, vibrational spectra include infrared spectra, such as near-infrared spectra, mid-infrared spectra, and / or far-infrared spectra.

[0045] In another aspect of one or more of the above methods, the spectral pattern is generated by Raman spectroscopy.

[0046] In another aspect of one or more of the above methods, the spectral pattern is at approximately 15,000 cm⁻¹. -1 Approximately 200cm -1 Measurements between.

[0047] In another aspect of the above-described methods, at least one of the one or more reference spectra is generated using samples from non-disease-affected subjects.

[0048] In another aspect of the above-described methods, at least one of the one or more reference spectra is generated using a sample from the diseased subject.

[0049] In another aspect of the above-described methods, at least one of the one or more reference spectra is generated using a cancerous sample.

[0050] In another aspect of one or more of the above methods, the disease is cancer.

[0051] In another aspect of one or more of the above methods, the cancer is breast cancer or colon cancer.

[0052] In another aspect of one or more of the above methods, the state is TNM staging.

[0053] In another aspect of the above-mentioned methods, one or more methods may also include treating the disease of the subject.

[0054] In another aspect of the above-described methods, at least one of the reference spectra is derived from one or more individuals belonging to the same group as the object.

[0055] In another aspect of one or more of the above methods, all reference spectra are derived from one or more individuals belonging to the same group as the object.

[0056] In another aspect of the above-described methods, at least one of the reference spectra is derived from one or more individuals belonging to a different group than the object.

[0057] In another aspect of one or more of the above methods, all reference spectra are derived from one or more individuals belonging to a different group than the object.

[0058] In another aspect of one or more of the methods described above, one or more reference spectra are derived from the object.

[0059] In another aspect of one or more of the above methods, the object is a human being.

[0060] In another aspect of one or more of the methods described above, the sample is in vitro.

[0061] In another aspect of one or more of the above methods, the sample includes blood, saliva, serum, plasma, urine, sputum, sweat, semen, synovial fluid, lymph, cerebrospinal fluid, biopsy sample, feces, or a combination thereof.

[0062] In another aspect of one or more of the above methods, the subject does not exhibit any disease symptoms.

[0063] In another aspect of one or more of the methods described above, the subject exhibits symptoms of disease.

[0064] In another aspect of one or more of the above methods, the subject has never had cancer or has a history of cancer.

[0065] In another aspect of one or more of the above methods, the subject exhibits one or more symptoms selected from breast pain, breast nodules, nipple discharge, weight loss, fatigue, anemia, and combinations thereof.

[0066] In another aspect of one or more of the above methods, the subject has the risk of developing breast cancer.

[0067] In another aspect of the above-mentioned methods, the subject is subjected to one or more assays for identifying breast cancer. Attached Figure Description

[0068] The accompanying drawings are intended to illustrate various examples of the terms, methods, and systems described herein, and are not intended to limit the scope of the teachings in any way. In the drawings:

[0069] Figure 1 These are schematic diagrams illustrating implementation methods for performing the methods described herein; and

[0070] Figure 2 This is a flowchart of an example method for diagnosing a disease of an object according to at least some implementations. Detailed Implementation

[0071] I. Definition

[0072] The term “non-subjective” used in this article in relation to screening, diagnosis and / or prognosis should be understood as meaning that visual examination of the sample and / or analysis of the spectra are not required to determine whether the sample was collected from a diseased or non-diseased patient.

[0073] II. Methods for screening, diagnosing, and / or prognosticating diseases

[0074] The subject can be a human or other animal, using molecular biomarkers from the subject sample. The method can be used, for example, with in vitro samples. Preferably, the method is non-invasive.

[0075] Currently, there is no need to know every specific molecular biomarker. This method can integrate all molecular biomarker profiles (which may be unique for a particular subject at a particular time) and correlate the results with a specific question, which can be a binary pattern, such as the presence or absence of breast cancer. If breast cancer is detected, its stage (grade) can also be assessed. Therefore, this method offers a significant improvement because it provides users with a concise and streamlined quantitative analysis tool to probe samples containing multiple molecules in complex environments and derive diagnoses and / or prognoses, without (i) requiring users to know the disease-specific molecular biomarkers in the sample and / or (ii) requiring no expertise in interpreting spectra. In some forms, knowing specific molecular biomarkers helps to correlate molecular profiles with specific biological modifications associated with the proposed binary question.

[0076] In some cases, the method involves analytical methods, computer implementation methods, or a combination thereof. In some forms, the method involves both analytical methods and computer implementation methods.

[0077] In some forms, the method involves (i) generating a spectral spectrum of an object sample using analytical methods, such that the spectral spectrum contains one or more components, (ii) obtaining an overall score of the spectral spectrum using computer-implemented techniques, and / or (iii) providing a diagnosis, prognosis, or both of the disease based on the overall score.

[0078] In some forms, calculating the overall score involves using all components of the spectrum. In other forms, calculating the overall score involves using only some components derived from the spectrum.

[0079] In some forms, the method involves screening for and diagnosing breast cancer by performing Raman measurements on human samples (particularly samples obtained in a non-invasive manner, such as using blood) and calculating a score based on the entire set or a portion of the Raman measurement results.

[0080] refer to Figure 1 The analytical method (e.g., spectrometry) can be performed in vitro. For a given sample, the method includes generating a spectral spectrum containing sample data (e.g., vibrational frequencies or intensities measured at specific vibrational frequencies) based on the spectrometry; assigning a score to the spectral spectrum by comparison, preferably, with a set of reference spectra containing data (e.g., vibrational frequencies or intensities measured at specific vibrational frequencies); and / or determining, by the score, whether the subject who obtained the sample has a disease, and optionally, if so, determining the stage (level) of the disease.

[0081] In some forms, the analytical method (such as the one described herein) involves a spectrometer and implements spectroscopic techniques, such as optical spectroscopy. In some forms, the spectrometer may be a Raman spectrometer with a frequency span of 3100 cm⁻¹. -1 Up to 900cm -1 Between, for example, 3050.855cm -1 Up to 929.527cm -1 Between (including endpoint values), and 1101 points.

[0082] In some forms, computer-based techniques (such as those described herein) can be used to determine a patient's disease state based on data acquired from a spectrometer. Preferably, the spectral data is a function of the molecular spectrum of the sample being analyzed.

[0083] Preferably, in some forms, the components of the sample's spectral spectrum and the components of one or more reference spectral spectra contain vibrational frequencies or measurements at specific vibrational frequencies. In some forms, the component scores can be summed to obtain an overall score. In some forms, the subject is considered to have the disease when the overall score is greater than a threshold; otherwise, the subject is considered not to have the disease. The overall score is used to determine whether a subject has breast cancer.

[0084] In some forms, at least one of the one or more reference spectral spectra is generated using a sample from a non-disease-prone patient. In some forms, at least one of the one or more reference spectral spectra is generated using a disease-prone sample. In some forms, at least one or more reference spectral spectra is generated using a cancer-prone sample. In some forms, the cancer-prone sample has breast cancer.

[0085] This approach can address the possibilities in screening, diagnosis, and / or prognosis using only a limited number of factors. For example, the classification of breast cancer uses only a limited number of factors: clinical stage, hormone receptors (estrogen and progesterone), HER-2 gene amplification, and cell proliferation (mitotic index or Ki-67). This can lead to the definition of large subgroups that are inherently heterogeneous, as breast cancer can be more complex for an individual patient. The Raman spectroscopy used in this paper enables the understanding of parts of the molecular reality (with reproducible spectral results for a given sample) by leveraging a broader range of biomarkers, some of which are specific to the disease or its biological consequences, and others that are host-specific. Computationally integrating these biomarkers through a series of tests allows for the differentiation of individual profiles when relevant questions are posed in a binary mode (a more comprehensive approach).

[0086] i. Analytical methods

[0087] The analytical methods include one or more spectroscopic techniques. Examples of spectroscopic techniques include, but are not limited to, field-resolved spectroscopy (e.g., field-resolved infrared spectroscopy), frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, and combinations thereof.

[0088] In some preferred forms, the spectroscopic techniques involve time-resolved spectroscopy or frequency-resolved spectroscopy.

[0089] In some forms, spectroscopic techniques involve vibrational spectroscopy. In others, vibrational spectroscopy includes infrared spectroscopy, such as near-infrared spectroscopy, mid-infrared spectroscopy, far-infrared spectroscopy, or Raman spectroscopy.

[0090] In some cases, spectroscopic methods detect chemical substructures within a molecule, rather than the entire molecule, by detecting vibrational responses to infrared or Raman excitations.

[0091] The spectrometer can operate over a range of vibrational frequencies. The frequency range is at (but not limited to) approximately 14,000 cm⁻¹. -1 Approximately 800cm -1Within the span between and within its subranges. In some forms, the spectrometer can be a broadband femtosecond-resolved broadband infrared laser source combined with an infrared sampling system for ultrasensitive molecular vibrational spectroscopy. In some forms, the frequency scan range is approximately 3050.855 cm⁻¹. -1 Approximately 929.527cm -1 Between. In some forms, the spectrometer can be a Raman spectrometer with a frequency range of 3050.855 cm⁻¹. -1 Up to 929.527cm -1 Between (including endpoint values), and 1101 points.

[0092] Preferably, the spectrometer uses high resolution. High spectral resolution may include 1 cm⁻¹. -1 Up to 10cm -1 Spectral sampling between, for example, 1cm -1 2cm -1 3cm -1 4cm -1 5cm -1 6cm -1 7cm -1 9cm -1 9cm -1 Or 10cm -1 .

[0093] ii. Computer Implementation Method

[0094] The computer implementation method described in this paper is not limited to any specific spectral analysis technique. This computer implementation method enables the generation of general spectral maps from data generated using field-resolved spectroscopy (e.g., field-resolved infrared spectroscopy), frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, and combinations thereof.

[0095] The computer implementation method can be executed on a computer capable of executing computer program instructions. For example, the computer may simultaneously have volatile and non-volatile memory (the latter used for initial storage of computer program instructions), input and output devices (e.g., a keyboard and a display), and one or more processors (e.g., a central processing unit (CPU), a graphics processing unit (GPU), etc.). The computer can be physically located close to the spectrometer that generates the data. Alternatively, the computer can be located remotely and connected to the spectrometer via Ethernet, Bluetooth, near-field communication, Wi-Fi, integrated circuits, or a combination thereof.

[0096] The embodiments describe non-limiting examples of the process performed by this technique. In short, the technique generates a spectral map containing one or more components, reflecting the molecular spectrum of the sample.

[0097] In a non-limiting example, the spectrum contains 1101 features, consisting of (3050.856 cm⁻¹) -1 -925.547cm -1 ) / (Spectral sampling (2cm) -1 The characteristic at each position in the spectrum corresponds to the photon count intensity at that wavenumber. Therefore, the length of the spectrum can be any value, but is limited by the frequency range and the spectral sampling of the instrument.

[0098] Typically, the raw data obtained through measurement consists of a two-dimensional vector, which is composed of wavenumber and intensity values, respectively. Further, the raw measurement vector Vr is processed in the following sequential steps: (i) smoothing the raw data; (ii) background removal; (iii) normalization; and (iv) determining the diseased and non-disease categories.

[0099] Now for reference Figure 2 The document illustrates an example method for diagnosing a disease of a subject according to at least some implementations. As described herein, method 200 can be performed by a computer.

[0100] At box 205, as described herein, a spectral spectrum is obtained; the components of the spectral spectrum of the object sample include vibrational frequencies (the reference spectrum also includes vibrational frequencies). For example, a spectral spectrum of the object sample and one or more reference spectra can be generated using data from a spectroscopic technique that uses a frequency sweep at (but not limited to) approximately 14,000 cm⁻¹. -1 Approximately 800cm -1 and its sub-scopes.

[0101] Spectroscopic maps of an object sample and one or more reference maps can be generated using data from spectroscopic techniques, including field-resolved spectroscopy (e.g., field-resolved infrared spectroscopy), frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, and combinations thereof.

[0102] Spectroscopic techniques may involve vibrational spectra, including infrared spectra, such as near-infrared, mid-infrared, and / or far-infrared spectra.

[0103] At box 210, the original vector (e.g., Vr) is identified from the spectral map, as described elsewhere in this document.

[0104] At box 215, Vr can be smoothed to generate a smoothed vector Vs. For example, data smoothing can be performed by applying the Savitzky-Golay algorithm with a polynomial order of 5 and a window size of 13.

[0105] At box 220, the average spectrum can be calculated based on a reference sample. For example, the reference sample could be the Raman spectra of six collected pure water samples, with a volume equal to the volume of the blood plasma droplets used to generate the spectrum to be analyzed. The average spectrum can be used as a reference background spectrum, which can be subtracted from or otherwise removed from the smoothing vector Vs to generate a corrected smoothing vector. Select the spectral region (e.g., approximately 300 to approximately 1900 cm⁻¹). -1 Between, or approximately 600 to approximately 1800 cm -1 Further analysis is performed within this region. A first average value of the plasma blood spectral intensity is calculated. A second average value of the background spectral intensity in the same region is also calculated. Next, the difference between the first and second average values ​​is calculated, and the result is added to the background spectrum to generate a corrected background spectrum. Finally, the difference between the plasma blood spectrum and the corrected background spectrum is calculated to obtain a smoothed and background-removed vector Vsb.

[0106] At box 225, the normalized vector Vn is obtained by dividing all intensity values ​​Vsb by the highest or lowest value among the top percentiles of the intensity values ​​(e.g., the top 1%, 2%, 3%, 4%, or 5%).

[0107] At box 230, obtain the subset vector Vf of Vn. This subset vector typically has 6 to 18 components. For example, in one example, the subset vector consists of 12 wavenumbers (wn) and their corresponding intensity values, which can be selected as follows: 620cm -1 Up to 670cm -1 Four wave numbers within the range, 720cm -1 Up to 760cm -1 Two wave numbers within the range, 1550cm -1 Up to 1580cm -1 Two wave numbers within the range, and 1740cm -1 Up to 1790cm -1 Four wavenumbers within the range. In some cases, the components of the subset vector are around 300cm. -1 Approximately 1900cm -1 Within a certain range. In some cases, the wavenumber of the subset vector is within a width of 20cm. -1 Up to 60cm -1 Within the non-overlapping range between them.

[0108] At box 235, the overall score is obtained using a polynomial function of degree 1, 2, 3, 4, 5, or 6 based on Vf, the coefficients of which are determined on a training dataset using a support vector machine (SVM). This score is further used to determine the patient's disease status, or to provide a probability of disease by applying Platt scaling to the score.

[0109] At box 240, the overall score is compared to one or more thresholds to determine the subject's disease / non-disease status. For example, when the overall score is greater than a threshold, the subject can be diagnosed with a disease. If the overall score contains more than one component, each component can be compared to its corresponding threshold.

[0110] In some cases, two reference spectra can be used to set the upper and lower threshold values. Therefore, the first reference spectrum sets the upper limit of the spectral data, and the second reference spectrum sets the lower limit of the spectral data.

[0111] The threshold can be determined using one or more reference spectral maps generated from samples of both diseased and non-diseased patients. The samples can be pre-labeled using existing methods. For example, the disease could be cancer, such as breast cancer, lung cancer, prostate cancer, colon cancer, skin cancer, blood cancers (leukemia, lymphoma, etc.), myeloma, and combinations thereof.

[0112] In some cases, at least one reference spectral profile can be selected from one or more individuals in a group that is the same as or similar to the subject, with the similarity of the group determined by demographic characteristics (e.g., similar age, sex, etc.). However, the groups need not be the same or similar in all cases.

[0113] Diagnostics can be provided at box 250.

[0114] In a non-restrictive example, the model can be of order one, and the disease state of vector Vr will be determined by calculating the distance from Vf to the model separating hyperplane and performing Pratt scaling on the result, and then using the value obtained in this way as a decision threshold to obtain a sensitivity in the range of at least 90% to at least 95%.

[0115] In some cases, the disease state calculated at box 235 is a TNM stage value. Those skilled in the art will understand that TNM refers to primary tumor (T), regional lymph node (N), and distant metastasis (M). TNM values ​​can be determined using the methods described above. In these embodiments, the reference spectral spectrum used for comparative analysis may have TNM values. Examples of TNM values ​​include: T1NXMX, T2NXMX, T3NXMX, etc.

[0116] III. How to Use

[0117] The methods described in this article can be used for the screening, diagnosis, and / or prognosis of breast cancer in humans or other animals.

[0118] The samples to be analyzed may include blood samples.

[0119] In some forms, the subject has no symptoms of disease. In some forms, the subject exhibits one or more symptoms of disease. These symptoms include, but are not limited to, breast pain, breast nodules, nipple discharge, weight loss, fatigue, anemia, or a combination thereof. In some forms, the subject has never had cancer or has a history of cancer.

[0120] In some forms, the subject is at risk of developing breast cancer (e.g., high risk). In some forms, the subject undergoes one or more tests to identify breast cancer.

[0121] Non-limiting examples involve using specific patterns from Raman measurements for breast cancer screening and diagnosis. In some forms, the method involves using a combination of specific Raman spectral measurement patterns of multiple molecular biomarkers for breast cancer screening and diagnosis. These molecular biomarkers can be detected in tissues or bodily fluids (e.g., blood, serum, plasma, urine) in patients with breast cancer. The format of a single Raman spectral measurement, called a spectrogram, contains a vector of thousands of variables, each measuring the molecular spectrum of a biological fluid at a given time point.

[0122] Any appropriate method can be used to assess the target directly in the biological sample (because in some cases the sample preparation step can be skipped).

[0123] In some forms, this method is used as part of a routine physical examination. Therefore, in some forms, the subject has not yet been diagnosed with breast cancer, and often for those specific forms, it is unclear whether the subject has a hyperplastic disease, such as a breast tumor. In other forms, the individual is at risk of developing breast cancer, is suspected of having breast cancer, or has a personal or family history of cancer (including breast cancer), and has risk factors such as BRCA1 / 2 mutations. In some forms, the individual may be known to have cancer, and the methods described herein can be used to determine the type, stage (grade), treatment response, and / or prognosis of breast cancer. In some forms, the individual has been diagnosed with breast cancer and may have undergone mastectomy, and / or may be monitored for breast cancer recurrence using the methods of this invention.

[0124] This method can also detect early and pre-disease states in subjects by detecting signals from low concentrations of analytes that indicate early or initial disease states. For example, this method can be used to detect the presence of abnormalities in samples below the levels detected by microscopic and spectroscopic examinations.

[0125] These methods can also be used to determine the stage (grade) of a diagnosed breast cancer. Furthermore, computer-implemented methods can be applied to measurements from any high-resolution spectrometer. The spectrometer can perform Fourier transform infrared spectroscopy, Raman spectroscopy, or any device that measures the relationship between infrared intensity or Raman scattering coefficients and vibrational frequencies.

[0126] Because of its streamlined process, this method reduces and / or eliminates subjectivity, eliminating the need for expert analysis of spectra and / or microscopic examination of samples. Therefore, it can provide high-quality testing and / or diagnostic services to underserved areas of the world. These methods also provide patients and physicians with immediate diagnostic decision-making, enabling immediate implementation of next steps and treatments. This means that patients and laboratory clinicians can almost immediately know whether the sample being tested came from a sick or healthy patient, and / or the stage (grade) of the disease (if applicable).

[0127] In some forms, these methods can be used to screen for and / or diagnose breast cancer, exhibiting remarkably high levels of specificity and sensitivity. In some forms, this high sensitivity is attributable to the medical advice provided by experts in interpreting test data, the expertise involved in the development and testing of advanced spectroscopic and / or computer-implemented technologies, and so on. This level can be significantly higher than previously implemented spectroscopic and / or microscopic methods.

[0128] Appropriate optical frequencies can be used to probe deeper tissues using non-invasive optical methods, and computer-aided techniques are well-suited for analyzing the output of these probes. The medical significance of this lies not only in the ability to collect immediate diagnostic information from a subject, but also in the capacity to extract more information from a wider range of areas by examining samples within the body—information far exceeding that obtained by removing biopsy samples or cells and then examining them. For example, tissue biopsies themselves can deform the remaining tissue, and bleeding accompanying a biopsy can distort a physician's observation of diseased tissue.

[0129] Table 1 shows a proof-of-concept clinical study of breast cancer (BC) from several patient samples that underwent Raman spectroscopy analysis. Table 1 presents the diagnostic results of breast cancer patients compared to control patients, which can be used to train and / or validate the Support Vector Machine (SVM) model described herein. Table 2 shows a proof-of-concept clinical study of colorectal cancer from multiple patient samples that underwent Raman spectroscopy analysis. Table 2 presents the diagnostic results of colorectal cancer patients compared to control patients, which can be used to train and / or validate the Support Vector Machine (SVM) model described herein.

[0130] As used in this article, the term “and / or” is intended to indicate an inclusive “or”. That is, for example, “X and / or Y” is intended to mean X or Y or both. As another example, “X, Y and / or Z” is intended to mean X or Y or Z or any combination thereof.

[0131] As used in this article, degree terms such as “basically,” “about,” and “approximately” refer to reasonable deviations from the terms they modify, deviations that do not significantly alter the result. These degree terms can also be understood to include deviations from the terms they modify, provided that such deviations do not negate the meaning of the terms they modify.

[0132] Any range of values ​​expressed as endpoints in this document includes all numbers and fractions within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, and 5). It should also be understood that all numbers and fractions are presumed to be modified by “about,” meaning that the referenced value may vary by up to a certain amount if the result does not change significantly.

[0133] The systems and methods described herein can be implemented in a combination of hardware and software. In some cases, the systems and methods described herein can be implemented at least in part using one or more computer programs that execute on one or more programmable devices, the programmable devices including at least one processing element and a data storage element (including volatile and non-volatile memory and / or storage elements). These systems may also have at least one input device (e.g., a button keyboard, mouse, touchscreen, etc.) and at least one output device (e.g., a display screen, printer, radio, etc.), depending on the nature of the device. Furthermore, in some examples, one or more systems and methods described herein can be implemented, or implemented as part of, a distributed or cloud-based computing system having multiple computing components distributed across a computing network. For example, a distributed or cloud-based computing system may correspond to a private distributed or cloud-based computing cluster associated with an organization.

[0134] Certain elements used to implement at least a portion of the systems, methods, and apparatuses described herein can be implemented by software written in a high-level procedural language (e.g., an object-oriented programming language). Therefore, the program code can be written in any suitable programming language (e.g., Python or Java). Alternatively, or otherwise, some of these software-implemented elements can be written in assembly language, machine language, or firmware as needed. In either case, the language can be a compiled language or an interpreted language.

[0135] At least some of these software programs may be stored on a storage medium (e.g., a computer-readable medium, such as, but not limited to, a read-only memory, a disk, or an optical disk) or on a device readable by a general-purpose or special-purpose programmable device. When the programmable device reads the software program code, the software program code configures the programmable device to operate in a new, specific, predefined manner, thereby performing at least one of the methods described herein.

[0136] Furthermore, at least a portion of the program associated with the systems and methods described herein may be distributed in a computer program product comprising a computer-readable medium carrying computer-usable instructions for one or more processors. This medium may be provided in various forms, including non-transitory forms, such as, but not limited to, one or more disks, optical discs, magnetic tapes, chips, and magnetic and electronic storage devices. Alternatively, the medium may be inherently transient, such as, but not limited to, wired transmission, satellite transmission, internet transmission (e.g., download), media, digital and analog signals, etc. The computer-usable instructions may also have various formats, including compiled code and uncompiled code.

[0137] The disclosed methods can be further understood through the following numbered clauses.

[0138] Clause 1. A method for diagnosing or monitoring a disease in a subject, the method comprising:

[0139] 1) Generate spectral maps of samples obtained from the object;

[0140] 2) Compare the spectrum with one or more reference spectra to obtain an overall score;

[0141] 3) Determine the disease status by comparing the overall score with a threshold.

[0142] Clause 2. The method according to Clause 1, wherein generating the spectral spectrum includes:

[0143] Obtain the original vector Vr, which is a two-dimensional vector consisting of multiple wavenumbers and multiple intensity values ​​corresponding to the multiple wavenumbers;

[0144] Filtering the original vector; and

[0145] The multiple intensity values ​​of the original vector are normalized to generate a normalized vector Vn.

[0146] Clause 3, the method described in Clause 2, wherein filtering includes smoothing the original vector and removing background noise.

[0147] Clause 4. The method described in Clause 2 or Clause 3, wherein the spectral map is further generated by selecting a subset vector (Vf) of the normalized vector Vn.

[0148] Clause 5, the method described in Clause 4, wherein the subset vector comprises 6 to 18 components.

[0149] Clause 6. The method described in Clause 5, wherein the wavenumber of the subset vector is at a width of 20cm -1 Up to 60cm -1 Within the non-overlapping range between them.

[0150] Clause 7. The method according to any one of Clauses 4 to 6, wherein the overall score is determined by a polynomial of degree 1 to 6 of the subset vectors, which has been previously determined on the training set by a support vector machine.

[0151] Clause 8. The method according to any one of Clauses 1 to 7, wherein the overall score is compared with a threshold.

[0152] Clause 9. The method according to any one of Clauses 1 to 7, wherein the threshold is adjusted to a higher value for diagnosing a disease, or adjusted to a lower value for monitoring a disease.

[0153] Clause 10, the method described in Clause 5, wherein Vf includes approximately 300cm -1 Approximately 1900cm -1 One or more values ​​within a range.

[0154] Clause 11, the method described in Clause 10, wherein Vf includes approximately 620cm -1 Approximately 670cm -1 The four values ​​between, approximately 720cm -1 Approximately 760cm -1 The two values ​​between, approximately 1550cm -1 Approximately 1580cm -1 The two values ​​between them are approximately 1740cm -1 Approximately 1790cm -1 The four values ​​between.

[0155] Clause 12. The method according to any one of Clauses 1 to 11, wherein the spectral spectrum is generated by vibrational spectroscopy, field-resolved spectroscopy, frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, or a combination thereof.

[0156] Clause 13. The method described in accordance with Clause 12, wherein the field-resolved spectrum includes field-resolved infrared spectrum.

[0157] Clause 14. The method described in Clause 12, wherein the vibrational spectrum includes infrared spectra, such as near-infrared spectra, mid-infrared spectra, resonant frequency spectra, and / or far-infrared spectra.

[0158] Clause 15. The method according to any one of Clauses 1 to 11, wherein the spectral pattern is generated by Raman spectroscopy.

[0159] Clause 16. The method according to any one of Clauses 12 to 15, wherein the spectral pattern is at approximately 15,000 cm⁻¹ -1 Approximately 200cm -1 Measurements between.

[0160] Clause 17. The method according to any one of Clauses 1 to 16, wherein at least one of the one or more reference spectra is generated using a sample from a non-disease subject.

[0161] Clause 18. The method according to any one of Clauses 1 to 17, wherein at least one of the one or more reference spectra is generated using a sample from a diseased subject.

[0162] Clause 19. The method according to any one of Clauses 1 to 17, wherein at least one of the one or more reference spectra is generated using a cancerous sample.

[0163] Clause 20. The method according to any one of Clauses 1 to 19, wherein the disease is cancer.

[0164] Clause 21, the method of claim 20, wherein the cancer is breast cancer or colon cancer.

[0165] Clause 22. The method described in accordance with Clause 20 or 21, wherein the status is TNM phasing.

[0166] Clause 23. The method according to any one of Clauses 1 to 22 further includes treating the disease of the subject.

[0167] Clause 24. The method according to any one of Clauses 1 to 23, wherein at least one of the one or more reference spectra is derived from one or more individuals belonging to the same group as the object.

[0168] Clause 25. The method described in Clause 24, wherein all reference spectra are derived from one or more individuals belonging to the same group as the object.

[0169] Clause 26. The method according to any one of Clauses 1 to 23, wherein at least one of the one or more reference spectra is derived from one or more individuals belonging to a different group than the object.

[0170] Clause 27. The method described in Clause 26, wherein all reference spectra are derived from one or more individuals belonging to a different group than the object.

[0171] Clause 28. The method according to any one of Clauses 1 to 27, wherein one or more reference spectra are derived from the object.

[0172] Clause 29. The method pursuant to any one of Clauses 1 to 28, wherein the object is a human being.

[0173] Clause 30. The method according to any one of Clauses 1 to 29, wherein the sample is in vitro.

[0174] Clause 31. The method according to any one of Clauses 1 to 30, wherein the sample includes blood, saliva, serum, plasma, urine, sputum, sweat, semen, synovial fluid, lymph, cerebrospinal fluid, biopsy sample, feces, or a combination thereof.

[0175] Clause 32. The method according to any one of Clauses 1 to 31, wherein the subject has no symptoms of disease.

[0176] Clause 33. The method according to any one of Clauses 1 to 32, wherein the subject exhibits symptoms of disease.

[0177] Clause 34. The method according to any one of Clauses 1 to 33, wherein the subject has never had cancer or has a history of cancer.

[0178] Clause 35. The method according to any one of Clauses 1 to 34, wherein the subject exhibits one or more symptoms selected from breast pain, breast nodules, nipple discharge, weight loss, fatigue, anemia, and combinations thereof.

[0179] Clause 36. The method according to any one of Clauses 1 to 35, wherein the subject is at risk of developing breast cancer.

[0180] Clause 37. The method according to any one of Clauses 1 to 36, wherein the subject is subjected to one or more assays for identifying breast cancer.

[0181] Clause 38. A method for screening and / or diagnosing a disease in a subject, the method comprising:

[0182] (i) Generate a spectral spectrum of the object sample, wherein the spectral spectrum includes components.

[0183] (ii) Using computer-based techniques to obtain an overall score for the spectra, and

[0184] (iii) Provide the diagnosis, prognosis, or both of the disease based on the overall score.

[0185] Clause 39. The method according to Clause 38, wherein diagnosis includes comparing an overall score with a threshold, wherein when the overall score is greater than the threshold, the subject is diagnosed with the disease.

[0186] Clause 40. The method according to Clause 38 or 39, wherein obtaining the overall score includes using computer-implemented techniques to generate one or more component scores by comparing the components of a spectral image with corresponding components in at least one of one or more reference spectral images.

[0187] Clause 41. The method according to Clause 40, wherein the overall score is obtained by summing one or more component scores, optionally using computer-implemented techniques for summing, wherein when only one component score is available, the overall score is that component score.

[0188] Clause 42. The method according to any one of Clauses 38 to 41, wherein a spectral spectrum of an object sample and one or more reference spectra are generated using data from a spectroscopic technique, the spectroscopic technique using a frequency scan at (but not limited to) about 14,000 cm⁻¹. -1 Approximately 800cm -1 and its sub-scopes.

[0189] Clause 43. The method according to any one of Clauses 38 to 42, wherein a spectral spectrum of an object sample and one or more reference spectra are generated using data from a spectroscopic technique, the frequency scan of which is at (but not limited to) about 14,000 cm⁻¹. -1 Approximately 800cm -1 and its sub-scopes.

[0190] Clause 44. The method according to any one of Clauses 38 to 43, wherein a spectral spectrum of an object sample and one or more reference spectra are generated using data from a spectroscopic technique, including field-resolved spectroscopy (e.g., field-resolved infrared spectroscopy), frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, and combinations thereof.

[0191] Clause 45. The method described in accordance with Clause 44, wherein the spectroscopic technique includes vibrational spectra.

[0192] Clause 46. The method described in accordance with Clause 45, wherein the vibrational spectrum includes infrared spectra, such as near-infrared spectra, mid-infrared spectra, resonant frequency spectra, and / or far-infrared spectra.

[0193] Clause 47. The method according to any one of Clauses 38 to 46, wherein the components of the spectral spectrum of the object sample include vibrational frequencies.

[0194] Clause 48. The method according to any one of Clauses 41 to 47, wherein at least one component of one or more reference spectra contains vibrational frequencies.

[0195] Clause 49. The method according to any one of Clauses 41 to 48, wherein at least one of the one or more reference spectra is generated using a sample from a non-disease patient.

[0196] Clause 50. The method according to any one of Clauses 41 to 49, wherein at least one of one or more reference spectra is generated using a sample from a patient with the disease.

[0197] Clause 51. The method according to any one of Clauses 41 to 49, wherein at least one of the one or more reference spectra is generated using a cancerous sample.

[0198] Clause 52, the method described in Clause 51, wherein the cancerous sample has a cancer selected from breast cancer, lung cancer, prostate cancer, colon cancer, skin cancer, blood cancer (e.g., leukemia and / or lymphoma), myeloma, and combinations thereof.

[0199] Clause 53. The method according to any one of Clauses 41 to 52, wherein at least one of the one or more reference maps is derived from one or more individuals belonging to the same group as the object.

[0200] Clause 54. The method according to any one of Clauses 41 to 51, wherein all reference spectra are derived from one or more individuals belonging to the same group as the object.

[0201] Clause 55. The method according to any one of Clauses 41 to 50, wherein at least one of the one or more reference spectra is derived from one or more individuals belonging to a different group than the object.

[0202] Clause 56. The method according to any one of Clauses 41 to 52 or 55, wherein all reference spectra are derived from one or more individuals belonging to a different group than the object.

[0203] Clause 57. The method according to any one of Clauses 41 to 56, wherein the overall score is obtained by comparing the spectrum with a first reference spectrum and a second reference spectrum.

[0204] Clause 58, the method described in accordance with Clause 57, wherein the first reference spectrum contains an upper limit of the spectral data.

[0205] Clause 59. The method described in accordance with Clause 57 or 58, wherein the second reference spectrum contains the lower limit of the spectral data.

[0206] Article 60. The method pursuant to any one of Articles 38 to 59, wherein the object is a human or other animal.

[0207] Clause 61. The method according to any one of Clauses 38 to 60, wherein the sample is in vitro.

[0208] Clause 62. The method according to any one of Clauses 38 to 61, wherein the disease includes breast cancer.

[0209] Clause 63. The method according to any one of Clauses 38 to 62, wherein the sample is selected from cells, blood, saliva, serum, plasma, urine, sputum, sweat, semen, synovial fluid, lymph, cerebrospinal fluid, biopsy sample, feces, and combinations thereof.

[0210] Clause 64. The method according to any one of Clauses 38 to 63, wherein the subject has no symptoms of disease.

[0211] Clause 65. The method according to any one of Clauses 38 to 63, wherein a diagnosis is made on an object exhibiting symptoms of a disease.

[0212] Clause 66. The method according to any one of Clauses 38 to 65, wherein the subject has never had cancer or has a history of cancer.

[0213] Clause 67. The method according to any one of Clauses 38 to 64 or 66, wherein the subject exhibits one or more symptoms selected from breast pain, breast nodules, nipple discharge, weight loss, fatigue, anemia, or a combination thereof.

[0214] Clause 68. The method pursuant to any one of Clauses 38 to 67, wherein the subject has a risk of developing breast cancer (e.g., high risk).

[0215] Clause 69. The method according to any one of Clauses 38 to 68, wherein the subject is subjected to one or more assays for identifying breast cancer.

[0216] Clause 70, the method according to any one of Clauses 38 to 69, wherein an overall score is used to determine whether a subject has breast cancer.

[0217] Clause 71. The method according to any one of Clauses 38 to 70, wherein the components are ordered by wavenumber.

[0218] Numerous specific details are set forth to provide a comprehensive understanding of the subject matter described herein. However, those skilled in the art will understand that the subject matter described herein can be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail to avoid obscuring the subject matter described herein. Any exemplary embodiments described herein do not limit any claim, and any claim may cover a process or system different from that claimed. The claims are not limited to systems or processes having all the features of any of the foregoing systems or processes, nor are they limited to features common to the plurality or all of the foregoing systems or processes. The foregoing systems or processes may not be embodiments for which any exclusive right is granted by this patent application. Any foregoing subject matter that is not granted exclusive right by this patent application may be subject matter of another protection document, such as a continuation patent application, and the applicant, inventor, or owner does not intend to waive, abandon, or publicly contribute such subject matter by disclosing it in this document.

[0219] Example

[0220] Example 1: Non-invasive diagnostic test for breast cancer detection using Raman spectroscopy.

[0221] The value of this method in cancer screening was obtained through Raman spectroscopy analysis of a series of liquid biopsy samples from 22 healthy controls (whose breast cancer was not detected by standard mammography screening) and 24 patients with stage I and II breast cancer (King Abdullah University of Science and Technology, Riyadh, Saudi Arabia) in the context of breast cancer. Raman spectroscopy analysis was performed by King Abdullah University of Science and Technology (KAUST) in Tuwah, Saudi Arabia. Non-hierarchical data mining correlation analysis was used to classify the spectra.

[0222] Several existing technologies enable high-throughput spectroscopic measurements of low concentrations of biomolecules, paving the way for early cancer detection and treatment monitoring.

[0223] Materials and methods

[0224] A research group on computational biology at King Abdullah University of Science and Technology, in collaboration with the King Abdullah University Medical City Cancer Center, studied a sample of 24 breast cancer patients and 22 healthy controls.

[0225] Experimental Analysis

[0226] Each sample was analyzed using Raman spectroscopy with a 10x dry objective lens, a laser wavelength of 785 nm, circular polarization, and a sample intensity of 50 mW. Spectra were acquired using a 600 gr / mm diffraction grating in the Raman spectrometer.

[0227] In summary, the collected blood samples were prepared and analyzed using Raman spectroscopy according to the following steps: Samples were collected into tubes provided for this purpose, up to 18.6 mL per patient (EDTA K3 tubes). The samples were incubated at room temperature until the blood clotted. The tubes were then centrifuged at 7000 rpm for 10 minutes at room temperature, and the supernatant was aliquoted into Eppendorf tubes (1 mL per serum aliquot) and stored at -80°C. No freeze-thaw cycles were performed. Three aliquots (3 mL total) were retained from each serum sample for multiple independent measurements. The tubes were then transported in batches to KAUST for cryopreservation using a temperature-controlled process and evaluated at KAUST. At KAUST, samples were stored in Eppendorf tubes at -80°C. The tubes were moved at -20°C for 2 minutes and then placed on ice for approximately 3 hours until thawed. The Raman microspectrometer was calibrated twice daily with a reference sample (silicon sample in our case). Take 40 μl of plasma from the Eppendorf tube and place it on a glass microscope slide that was previously covered with aluminum foil. Micro Slides (90° ground edge, 1mm thickness). Using the above configuration, two spectra were collected for each drop of plasma, with an integration time of 30 seconds, a cumulative count of 20, and a spectral range of 0 cm⁻¹. -1 Up to 2300cm -1 Two additional plasma drops were subjected to two separate spectral measurements, for a total of three drops (40 μl per drop) from each patient sample. After spectral acquisition, the raw data were smoothed using the Savitzky-Golay algorithm (5th-order polynomial, window size 13). Baseline was reduced by subtracting the water spectrum, which was obtained by averaging six water spectra acquired under the same plasma conditions (40 μl on a microscope slide covered with aluminum foil). Baseline removal was performed using the free software Raman Tool Set.

[0228] Table 1: Clinical data of breast cancer from the KSU study obtained by Raman spectroscopy analysis

[0229]

[0230]

[0231]

[0232] Table 1 (continued)

[0233]

[0234]

[0235]

[0236] Table 1 (continued)

[0237]

[0238]

[0239] Table 1 (continued)

[0240]

[0241]

[0242]

[0243] Table 1 (continued)

[0244]

[0245]

[0246]

[0247] Table 1 (continued)

[0248]

[0249]

[0250]

[0251] Table 1 (continued)

[0252]

[0253]

[0254]

[0255] Table 2: Clinical data of colorectal cancer from KSA obtained by Raman spectroscopy (collected at KSA trial site number 101-001)

[0256]

[0257]

[0258]

[0259]

[0260] Table 2 (continued)

[0261]

[0262]

[0263]

[0264]

Claims

1. A method for diagnosing or monitoring a disease in a subject, the method comprising: 4) Generate the spectral spectrum of the sample obtained from the object; 5) Compare the spectrum with one or more reference spectra to obtain an overall score; 6) The disease status is determined by comparing the overall score with a threshold.

2. The method according to claim 1, wherein generating the spectral spectrum comprises: Obtain the original vector Vr, which includes a two-dimensional vector of multiple wavenumbers and multiple intensity values ​​corresponding to the multiple wavenumbers; Filter the original vector; as well as The plurality of intensity values ​​of the original vector are normalized to generate a normalized vector Vn.

3. The method of claim 2, wherein the filtering includes smoothing the original vector and removing background noise.

4. The method of claim 2, wherein the spectral map is further generated by selecting a subset vector (Vf) of the normalized vector Vn.

5. The method of claim 4, wherein the subset vector comprises 6 to 18 components.

6. The method according to claim 5, wherein the wavenumber of the subset vector is at a width of 20cm -1 Up to 60cm -1 Within the non-overlapping range between them.

7. The method of claim 4, wherein the overall score is determined by a polynomial of degree 1 to 6 of the subset vector.

8. The method of claim 1, wherein the overall score is compared with the threshold.

9. The method of claim 1, wherein the threshold is adjusted to a higher value for diagnosing a disease, or adjusted to a lower value for monitoring a disease.

10. The method of claim 5, wherein Vf comprises about 300 cm -1 Approximately 1900cm -1 One or more values ​​within a range.

11. The method of claim 10, wherein Vf comprises approximately 620 cm -1 Approximately 670cm -1 The four values ​​between, approximately 720cm -1 Approximately 760cm -1 The two values ​​between, approximately 1550cm -1 Approximately 1580cm -1 The two values ​​between them and approximately 1740cm -1 Approximately 1790cm -1 The four values ​​between.

12. The method according to claim 1, wherein the spectral spectrum is generated by vibrational spectroscopy, field-resolved spectroscopy, frequency-resolved spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, infrared attenuated total reflectance, diffuse reflectance spectroscopy, or a combination thereof.

13. The method of claim 12, wherein the field-resolved spectrum comprises field-resolved infrared spectrum.

14. The method of claim 12, wherein the vibrational spectrum includes infrared spectra, such as near-infrared spectra, mid-infrared spectra, resonance frequency spectra, and / or far-infrared spectra.

15. The method of claim 1, wherein the spectral pattern is generated by Raman spectroscopy.

16. The method of claim 12, wherein the spectrum is at approximately 15,000 cm⁻¹ -1 Approximately 200cm -1 Measurements between.

17. The method of claim 1, wherein at least one of the one or more reference spectra is generated using a sample from a non-disease subject.

18. The method of claim 1, wherein at least one of the one or more reference spectra is generated using a sample from a diseased subject.

19. The method of claim 1, wherein at least one of the one or more reference spectra is generated using a cancerous sample.

20. The method of claim 1, wherein the disease is cancer.

21. The method of claim 20, wherein the cancer is breast cancer or colon cancer.

22. The method of claim 20, wherein the state is a TNM stage.

23. The method of claim 1, further comprising treating the disease of the object.

24. The method of claim 1, wherein at least one of the one or more reference spectra is derived from one or more individuals belonging to the same group as the object.

25. The method of claim 24, wherein all reference spectra are derived from one or more individuals belonging to the same group as the object.

26. The method of claim 1, wherein at least one of the one or more reference spectra is derived from one or more individuals belonging to a different group than the object.

27. The method of claim 26, wherein all reference spectra are derived from one or more individuals belonging to a different group than the object.

28. The method of claim 1, wherein the one or more reference spectra are derived from the object.

29. The method of claim 1, wherein the object is a human being.

30. The method of claim 1, wherein the sample is in vitro.

31. The method of claim 1, wherein the sample comprises blood, saliva, serum, plasma, urine, sputum, sweat, semen, synovial fluid, lymph, cerebrospinal fluid, biopsy sample, feces, or a combination thereof.

32. The method of claim 1, wherein the subject has no symptoms of disease.

33. The method of claim 1, wherein the subject exhibits disease symptoms.

34. The method of claim 1, wherein the subject has never had cancer or has a history of cancer.

35. The method of claim 1, wherein the subject exhibits one or more symptoms selected from breast pain, breast nodules, nipple discharge, weight loss, fatigue, anemia, and combinations thereof.

36. The method of claim 1, wherein the subject has a risk of developing breast cancer.

37. The method of claim 1, wherein the object is subjected to one or more assays for identifying breast cancer.

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