A tumor sampling and tumor marker detection system

Abstract

The application relates to the technical field of tumor marker detection, in particular to a tumor sampling and tumor marker detection system. The system comprises: collecting concentration values; after classifying patients, clustering the patients based on the concentration values, and determining the abnormalities of each tumor marker; then analyzing the progress values of all the detected tumor markers; then analyzing the fluctuation conditions based on the progress values, calculating a risk assessment index according to the proportion of fluctuation conditions of the patients in the same category and a fluctuation index; determining the interval length of the next time through the risk assessment index; and re-performing tumor marker detection after the adjusted interval length. The application solves the problem of time misjudgment caused by false progress.

Description

Technical Field

[0001] The present application relates to the technical field of tumor marker detection, and in particular to a tumor sampling and tumor marker detection system. Background Art

[0002] Existing methods for tumor sampling and tumor marker testing in ovarian cancer patients typically use the results of a single sampling test combined with a fixed predictive model to identify patients in different risk groups. Testing frequency is then adjusted based on the patient's risk of recurrence. For example, in ovarian cancer, fluid sampling is typically performed to obtain the patient's absolute ctDNA concentration and CA125 test values. These values ​​are then input into a fixed predictive model to determine the patient's risk assessment, which in turn adjusts the patient's testing frequency.

[0003] However, existing methods often show no actual tumor progression. Directly inputting the test data into a fixed prediction model may lead to a large deviation in the patient's risk index. In actual testing, there are cases of false progression, which will lead to large errors in the patient's risk assessment and inaccurate adjustment of the patient's testing frequency. Summary of the Invention

[0004] In order to solve the technical problem of inaccurate detection frequency, this application provides a tumor sampling and tumor marker detection system. The technical solutions adopted are as follows:

[0005] This application proposes a tumor sampling and tumor marker detection system, which includes the following modules:

[0006] An acquisition module collects the concentration values ​​of multiple tumor markers of the patient at preset time intervals;

[0007] The anomaly detection module categorizes patients into multiple groups based on tumor properties, histological type, and degree of differentiation. In a single test, all patients in each group are clustered based on their concentration values. An abnormality index for each tumor marker is calculated for each patient based on the number of patients in their cluster and the ratio of a patient's tumor marker concentration to the maximum value of that tumor marker.

[0008] The progress value acquisition module sorts the abnormal index of each tumor marker in time sequence to obtain an abnormal time series sequence; the progress value of each test is obtained based on the average value of the abnormal index at each test, the time from the moment when the abnormal index increased before each test to the most recent surgery, the number of moments when the abnormal index increased, and the average value of the abnormal index increase;

[0009] The risk assessment module determines the fluctuation index of each test based on the difference in progress values ​​between adjacent tests for each patient, and determines the fluctuation test based on the fluctuation index; calculates the assessment index based on the recovery time ratio of the fluctuation test and the fluctuation index; and obtains the risk assessment index of each test based on the average of the fluctuation test assessment index before each test and the proportion of the number of fluctuation tests of the patient among patients of the same type;

[0010] The detection interval adjustment module determines the interval between the current detection and the next detection based on the interval between the current detection and the previous detection and the risk assessment index of the current detection; and re-detects tumor markers after the adjusted interval.

[0011] In the above scheme, the present application combines the concentrations of multiple tumor markers obtained from the patient's fluid sampling results to preliminarily obtain the degree of progression of the patient's disease, and combines the fluctuation differences of the patient's own historical test data to analyze the possibility of false disease progression in the current situation. Based on the above results and the recurrence of the patient's disease, the patient's risk assessment index in the current state is obtained, thereby issuing an early warning for situations with higher risks, and dynamically adjusting the patient's testing frequency to make the testing time more reasonable, thereby providing a reference for medical staff and preventing misjudgment.

[0012] In one embodiment, the tumor markers include ctDNA, CA125, and HE4.

[0013] In one embodiment, the cluster distance is the Mahalanobis distance between data points corresponding to two patients; each dimension of the data point is a concentration value of a tumor marker.

[0014] In one embodiment, the abnormal index of the tumor marker is negatively correlated with the number of data points in the cluster where the patient is located, and is positively correlated with the ratio of the concentration value of the tumor marker to the maximum concentration value of the tumor marker of this type.

[0015] In one embodiment, the method for obtaining the progress value at each detection based on the average value of the abnormal index at each detection, the time from the moment when the abnormal index increased before each detection to the most recent surgery, the number of moments when the abnormal index increased, and the average value of the abnormal index increase is:

[0016] , represents the mean abnormal index of the tumor marker of the i-th patient at the k-th detection, It represents the time between the last valid abnormal moment and the most recent surgery of the i-th patient before the k-th detection. represents the number of valid abnormal moments before the kth detection of the i-th patient, represents the number of tests of the i-th patient before the k-th test, represents the mean of all valid abnormal growth values ​​of the i-th patient before the k-th test; represents the possibility that the i-th patient has true progression at the k-th test;

[0017] The corresponding probability is obtained each time the patient is tested, and is normalized using a normalization method and recorded as the progress value of each test.

[0018] In one embodiment, the effective abnormal growth value is a differential value greater than 0 after the abnormal time series is subjected to a first-order difference; the moment corresponding to the effective abnormal growth value is recorded as the effective abnormal growth moment; if all tumor markers are effective abnormal growth moments at the same moment, then the moment is recorded as the effective abnormal moment.

[0019] In one embodiment, the method for determining the fluctuation index of each test based on the difference in progress values ​​between adjacent tests of each patient is:

[0020] , represents the progress value of the i-th patient at the k-th test, represents the progress value of the i-th patient at the k-1th test, represents the fluctuation index of the i-th patient at the k-th test.

[0021] In one embodiment, the evaluation index is negatively correlated with the recovery time ratio and negatively correlated with the fluctuation index; if the progress value of a certain detection after the fluctuation detection is less than the average of all progress values, it indicates that the fluctuation of the fluctuation detection has recovered.

[0022] In one embodiment, the risk assessment index is negatively correlated with the proportion of the patient's fluctuation detection times among patients of the same type, and is positively correlated with the mean value of the assessment index.

[0023] In one embodiment, the method for determining the interval between the current detection and the next detection based on the interval between the current detection and the previous detection and the risk assessment index of the current detection is:

[0024] , represents the risk assessment index of the i-th patient at the k-th test, represents the risk assessment index of the i-th patient at the k-1th test, It represents the interval between the k-1th test and the next test of the i-th patient, It represents the interval between the kth test of the i-th patient and the next test.

[0025] The beneficial effects of this application are:

[0026] This application combines the concentrations of multiple tumor markers obtained from the patient's fluid sampling results to preliminarily obtain the degree of progression of the patient's disease, and combines the fluctuation differences of the patient's own historical test data to analyze the possibility of false progression of the disease in the current situation. Based on the above results and the recurrence of the patient's disease, the patient's risk assessment index in the current state is obtained, thereby issuing an early warning for higher-risk situations and dynamically adjusting the patient's testing frequency, thereby providing a reference for medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 A flow chart of a tumor sampling and tumor marker detection system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a tumor sampling and tumor marker detection system proposed in this application. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0031] An embodiment of a tumor sampling and tumor marker detection system:

[0032] The following describes in detail a specific scheme of a tumor sampling and tumor marker detection system provided by the present application with reference to the accompanying drawings.

[0033] See also Figure 1 , which shows a flow chart of a tumor sampling and tumor marker detection system provided by an embodiment of the present application. The system includes the following modules: an acquisition module, an abnormality detection module, a progress value acquisition module, a risk assessment module, and a detection interval adjustment module.

[0034] Acquisition module: This application performs tumor sampling and tumor marker testing on patients, and analyzes the patient's risks at different times based on the test results and the patient's recovery status to assess the patient's risk.

[0035] Tissue sampling was performed on all patients to obtain the nature, histological type, and degree of differentiation of each patient's tumor; the patient samples were all ovarian cancer patients in the hospital.

[0036] Each patient undergoes tumor marker testing to obtain tumor marker concentrations, which are then normalized to obtain a concentration value. In this example, the concentrations of three tumor markers, ctDNA (circulating tumor DNA), CA125 (cancer antigen 125), and HE4 (human epididymis protein 4), are obtained through fluid sampling. The frequency of tumor marker testing for each patient is determined by the individual; in this example, testing is performed every three months, using linear normalization.

[0037] Obtaining ctDNA involves collecting 10-20 mL of peripheral blood using dedicated cfDNA storage tubes (such as Streck tubes or EDTA tubes) to avoid hemolysis. Plasma is then separated and stored within 6 hours of blood collection by low-speed centrifugation (e.g., 1600 × g for 10 minutes) to remove blood cells and then high-speed centrifugation (e.g., 16,000 × g for 10 minutes) to remove residual cell debris. This purified plasma is then immediately frozen at -80°C.

[0038] CA125 acquisition includes: drawing 5 L of fasting venous blood from the patient in the morning, anticoagulating it, centrifuging it at 3000 r / min for 10 minutes, and storing the supernatant in a -20℃ refrigerator.

[0039] HE4 collection involves collecting venous blood (usually 3-5 mL) using EDTA or heparinized tubes. Separate plasma or serum by centrifugation (1600 × g, 10 minutes) to avoid hemolysis. Store at room temperature (2-8°C).

[0040] At this point, the concentration value of each tumor marker for each patient was obtained.

[0041] The abnormality detection module typically requires regular dynamic monitoring of tumor markers during ovarian cancer treatment and recovery. Tumor marker values ​​fluctuate over time for different patients. For example, a decrease in ctDNA concentration typically indicates recovery. However, inflammation, trauma, or recurrence risk can cause ctDNA values ​​to rise to a certain degree. Therefore, analysis is performed based on tumor marker concentrations.

[0042] The nature of the tumor includes benign, malignant, and borderline; the histological type includes epithelial tumor, germ cell tumor, sex cord-stromal tumor, and metastatic tumor; and the degree of differentiation includes well-differentiated, moderately differentiated, and poorly differentiated.

[0043] Patients with the same three indicators are grouped as one category, for example, all patients whose tumors are malignant in nature, whose histological types are epithelial tumors, and whose differentiation levels are well differentiated are grouped as one category.

[0044] The tumor marker detection intervals for each type of patient were analyzed. When the patient's condition progressed well, the corresponding tumor marker concentration decreased to a certain extent. Based on this, a multidimensional sample space of tumor markers was established, in which the concentration of each tumor marker corresponded to a dimension. Taking a single tumor marker as an example, the direction of increase in its concentration value is the positive direction of that dimension.

[0045] The concentration value of the tumor marker of each patient at each test constitutes a data point, and the data points of all patients of the same type tested at the same time are clustered; in this embodiment, all clustering methods are k-means clustering.

[0046] Since the concentrations of tumor markers have a certain correlation, directly calculating by spatial position ignores the correlation between dimensions. Therefore, the cluster distance for clustering is based on the spatial distance between data points and the correlation between dimensions. In this embodiment, the cluster distance is the Mahalanobis distance.

[0047] In this embodiment, the k value of the cluster is obtained by rounding up the ratio of the number of patients to the maximum cluster distance.

[0048] Based on distance, each patient type is divided into multiple clusters. The clustering results are projected onto the dimensions of each tumor marker. Taking a single dimension as an example, the probability of abnormality in each patient's test in that dimension is analyzed: if the number of data points in the cluster of a point in the current sample space is small, it indicates that the cluster in which the point belongs is significantly different from the overall cluster. If the concentration of the marker corresponding to the point is higher than the concentrations of the others, then the patient's indicator in that dimension is relatively abnormal during the current test. This abnormality reflects the progression of the tumor.

[0049] Based on the above analysis, the abnormality index of each tumor marker for each patient in each test is obtained. The abnormality index of the tumor marker is negatively correlated with the number of data points in the cluster where the patient is located, and is positively correlated with the ratio of the tumor marker concentration to the maximum concentration.

[0050] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large; the specific relationship is determined by actual application and this application does not impose any special restrictions.

[0051] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by actual application and this application does not impose any special restrictions.

[0052] Preferably, at each detection, the expression of the abnormality index is:

[0053] , represents the concentration value of the oth tumor marker of the i-th patient, represents the number of data points in the cluster where the i-th patient is located, represents the maximum concentration value of the oth tumor marker, represents the abnormal index of the oth tumor marker of the i-th patient.

[0054] The greater the concentration value of each patient's tumor marker compared to the maximum tumor marker, the more serious the tumor deterioration, that is, the more abnormal it is.

[0055] At this point, the abnormal index of each tumor marker for each patient was obtained at each test.

[0056] Progress value acquisition module: During the tumor marker detection process of ovarian cancer patients, the marker concentration may fluctuate over time. Generally, when the tumor marker concentration increases, the patient's tumor development may worsen, but non-tumor factors may also cause the above-mentioned abnormalities in tumor marker concentration.

[0057] For example, for ctDNA testing, after the patient receives immunotherapy, the absolute concentration of ctDNA increases when immune cells attack tumor cells, but as the subsequent tumor burden decreases, the ctDNA concentration will gradually decrease. When the tumor condition worsens, the abnormality of ctDNA persists and is relatively stable. For CA125 testing, it is easily affected by factors such as the patient's menstrual period or pelvic inflammatory disease, resulting in large fluctuations in concentration. However, when the tumor condition worsens, the concentration usually rises steadily (that is, the abnormality of this indicator continues to be high). Among them, when the patient has pelvic inflammatory disease, if the tumor develops stably, the CA125 concentration may increase while the ctDNA remains unchanged or decreases.

[0058] Therefore, abnormal tumor marker concentrations may be due to either non-tumor factors or tumor factors. True progression is caused by tumor factors, while pseudo-progression is caused by non-tumor factors.

[0059] When pseudo-progression occurs, the concentration of a single tumor marker usually increases to a peak within a short period of time after treatment or surgery, and then continues to decrease. When true progression occurs, the concentration of the tumor marker continues to increase.

[0060] Therefore, if the abnormal indexes of various tumor markers of a patient increase simultaneously as of a certain test, and the increase lasts for a long time, the degree of abnormality is large, and the increase in the abnormal index occurs far away from the time of surgery, immunotherapy, or chemotherapy, then the patient is more likely to have a true disease progression.

[0061] For each patient, the abnormal index detected by a tumor marker is arranged in time sequence to obtain an abnormal time series;

[0062] Perform first-order difference on the abnormal time series, record the difference value greater than 0 as the effective abnormal growth value, and the corresponding moment as the effective abnormal growth moment.

[0063] If a moment is a valid abnormal growth moment under all tumor markers, then this moment is recorded as a valid abnormal moment; for any test, it is recorded as the kth test. Before the kth test, the more valid abnormal moments there are, the larger the corresponding valid abnormal growth value. The longer the time from the last valid abnormal moment to the patient's most recent surgery, the greater the degree of abnormality, and the greater the possibility that the patient has true progression at the kth test.

[0064] Based on this, the probability of the patient having true progression in the kth test is obtained, and its expression is:

[0065] , represents the mean abnormal index of the tumor marker of the i-th patient at the k-th detection, It represents the time between the last valid abnormal moment and the most recent surgery of the i-th patient before the k-th detection. represents the number of valid abnormal moments before the kth detection of the i-th patient, represents the number of tests of the i-th patient before the k-th test, represents the mean of all valid abnormal growth values ​​of the i-th patient before the k-th test; represents the probability that the i-th patient has true progression at the k-th test.

[0066] The corresponding probability is obtained each time the patient is tested, and is normalized using a normalization method and recorded as the progress value of each test.

[0067] At this point, the progress value of each patient at each test was obtained.

[0068] In the risk assessment module, during the recovery process of tumor treatment, the indicators obtained by the above steps may fluctuate briefly. For example, under treatment pressure, different clonal subpopulations have different sensitivities to treatment, and some subpopulations may proliferate briefly, causing the obtained progression value to increase to a certain extent. However, as the immune system or treatment controls the tumor clones, under normal circumstances, the possibility of true progression of the patient's corresponding tumor may gradually decline. The faster the decline, the stronger the patient's recovery ability may be, and the more favorable the prognosis, and the smaller the patient's risk assessment value at the time of the current test, and vice versa.

[0069] For each patient, if the patient's progress value fluctuates less than that of the same type of patients, the fluctuation amplitude is smaller, and the value can fall back quickly after the fluctuation, then the corresponding risk assessment value of the patient is smaller.

[0070] The progress value of each patient is sorted in time sequence to obtain a progress time series. When the disease is well controlled, the progress value should gradually decrease or remain stable over time.

[0071] Therefore, the fluctuation index of each test is obtained based on the difference between the progress value of each test and the progress value of the previous test.

[0072] Preferably, in this embodiment, the expression of the volatility index is:

[0073] , represents the progress value of the i-th patient at the k-th test, represents the progress value of the i-th patient at the k-1th test, represents the fluctuation index of the i-th patient at the k-th test.

[0074] After normalizing the fluctuation index of each detection, the detection corresponding to the value greater than 0.7 of the normalized result is regarded as fluctuation detection, and the rest are non-fluctuation detection; in this embodiment, the normalization method is a linear normalization method.

[0075] For any fluctuation detection, if starting from this fluctuation, the patient The shorter the time taken to return to the normal level before the fluctuation, the faster the recovery speed of the fluctuation. In this embodiment, the normal level is obtained by calculating the average of all progress values ​​of the patient.

[0076] For the subsequent tests of the fluctuation test, if the progress value in one of the tests is less than the average of all the progress values, it means that the fluctuation of the fluctuation test has returned to a normal level in this test.

[0077] Therefore, based on the recovery time ratio of the fluctuation and the fluctuation index, the evaluation index of the patient at the wth fluctuation detection is obtained. The evaluation index is negatively correlated with the recovery time ratio and negatively correlated with the fluctuation index.

[0078] Preferably, the expression of the evaluation index is:

[0079] , represents the fluctuation index of the i-th patient at the w-th fluctuation test, represents the recovery time of the i-th patient at the w-th fluctuation detection, It represents the time from the wth fluctuation detection to the next fluctuation detection of the i-th patient, represents the evaluation index of the i-th patient at the w-th fluctuation detection.

[0080] If there is no next fluctuation detection, then It represents the duration of time from the wth fluctuation detection of the i-th patient to the current moment. The larger the value, the longer the patient maintains stability. The shorter the recovery time, the faster the recovery speed of this fluctuation.

[0081] For the kth test, the risk assessment index of the patient at the kth test is determined by the evaluation index of the overall fluctuation test before the kth test and the proportion of the number of fluctuation tests of the patient among similar patients.

[0082] The risk assessment index was negatively correlated with the proportion of the number of fluctuation tests among similar patients, and positively correlated with the overall assessment index.

[0083] Preferably, in this embodiment, the risk assessment index is expressed as:

[0084] , represents the mean number of fluctuation tests for all patients of the same type as the i-th patient before the k-th test, represents the number of fluctuation tests of the i-th patient before the k-th test, represents the mean of the evaluation index of all fluctuation tests of the i-th patient before the k-th test, represents the normalization function, represents the risk assessment index of the i-th patient at the k-th test.

[0085] The fewer the number of fluctuation tests for the patient compared with the same type of patients, the more stable the patient is.

[0086] At this point, the risk assessment index of each patient at each test is obtained.

[0087] The test interval adjustment module obtains the patient's risk assessment value for each test through the above steps. If the risk assessment value of the current test is significantly greater than the risk assessment value of the previous test, it indicates that the patient is more likely to be at risk. The difference between the risk assessment index of the previous test and the risk assessment index of the current test is calculated. If the difference is negative, it indicates that the patient's risk level has increased, and the interval between the two tests should be shortened. In this embodiment, the interval is 3 months.

[0088] This obtains the interval time from the patient to the next test, expressed as:

[0089] , represents the risk assessment index of the i-th patient at the k-th test, represents the risk assessment index of the i-th patient at the k-1th test, It represents the interval between the k-1th test and the next test of the i-th patient, It represents the interval between the kth test of the i-th patient and the next test.

[0090] Patients were tested for tumor markers based on the adjusted intervals.

[0091] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

[0092] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A tumor sampling and tumor marker detection system, characterized in that: The system includes the following modules: An acquisition module collects the concentration values ​​of multiple tumor markers of the patient at preset time intervals; The anomaly detection module categorizes patients into multiple groups based on tumor properties, histological type, and degree of differentiation. In a single test, all patients in each group are clustered based on their concentration values. An abnormality index for each tumor marker is calculated for each patient based on the number of patients in their cluster and the ratio of a patient's tumor marker concentration to the maximum value of that tumor marker. The progress value acquisition module sorts the abnormal index of each tumor marker in time sequence to obtain an abnormal time series sequence; the progress value of each test is obtained based on the average value of the abnormal index at each test, the time from the moment when the abnormal index increased before each test to the most recent surgery, the number of moments when the abnormal index increased, and the average value of the abnormal index increase; a risk assessment module that determines a fluctuation index for each test based on a difference in progress values ​​between adjacent tests for each patient, and determines a fluctuating test based on the fluctuation index; The evaluation index is calculated based on the recovery time ratio and the fluctuation index of the fluctuation test; the risk assessment index of each test is obtained based on the average of the fluctuation test evaluation index before each test and the ratio of the number of fluctuation tests of the patient among patients of the same type; The detection interval adjustment module determines the interval between the current detection and the next detection based on the interval between the current detection and the previous detection and the risk assessment index of the current detection; and re-detects tumor markers after the adjusted interval.

2. A tumor sampling and tumor marker detection system according to claim 1, characterized in that: The tumor markers include ctDNA, CA125 and HE4.

3. The tumor sampling and tumor marker detection system according to claim 1, wherein: The cluster distance is the Mahalanobis distance between the data points corresponding to two patients; each dimension of the data point is the concentration value of a tumor marker.

4. A tumor sampling and tumor marker detection system according to claim 3, characterized in that: The abnormal index of the tumor marker is negatively correlated with the number of data points in the cluster where the patient is located, and is positively correlated with the ratio of the concentration value of the tumor marker to the maximum concentration value of the tumor marker of this type.

5. The tumor sampling and tumor marker detection system according to claim 1, wherein: The method for obtaining the progress value at each detection based on the average value of the abnormal index at each detection, the time between the moment when the abnormal index increased before each detection and the most recent surgery, the number of moments when the abnormal index increased, and the average value of the abnormal index increase is: , represents the mean abnormal index of the tumor marker of the i-th patient at the k-th detection, It represents the time between the last valid abnormal moment and the most recent surgery of the i-th patient before the k-th detection. represents the number of valid abnormal moments before the kth detection of the i-th patient, represents the number of tests of the i-th patient before the k-th test, represents the mean of all valid abnormal growth values ​​of the i-th patient before the k-th test; represents the possibility that the i-th patient has true progression at the k-th test; The corresponding probability is obtained each time the patient is tested, and is normalized using a normalization method and recorded as the progress value of each test.

6. The tumor sampling and tumor marker detection system according to claim 5, characterized in that: The effective abnormal growth value is the differential value greater than 0 after the first-order difference of the abnormal time series; the moment corresponding to the effective abnormal growth value is recorded as the effective abnormal growth moment; if all tumor markers are effective abnormal growth moments at the same moment, then this moment is recorded as the effective abnormal moment.

7. The tumor sampling and tumor marker detection system according to claim 1, wherein: The method for determining the fluctuation index of each test based on the difference in progress values ​​between adjacent tests of each patient is: , represents the progress value of the i-th patient at the k-th test, represents the progress value of the i-th patient at the k-1th test, represents the fluctuation index of the i-th patient at the k-th test.

8. The tumor sampling and tumor marker detection system according to claim 1, wherein: The evaluation index is negatively correlated with the recovery time ratio and the fluctuation index; if the progress value of a certain test after the fluctuation detection is less than the average of all progress values, it means that the fluctuation of the fluctuation detection has recovered.

9. The tumor sampling and tumor marker detection system according to claim 1, wherein: The risk assessment index is negatively correlated with the proportion of the patient's fluctuation detection times among patients of the same type, and is positively correlated with the mean value of the assessment index.

10. The tumor sampling and tumor marker detection system according to claim 1, wherein: The method for determining the interval between the current detection and the next detection based on the interval between the current detection and the previous detection and the risk assessment index of the current detection is: , represents the risk assessment index of the i-th patient at the k-th test, represents the risk assessment index of the i-th patient at the k-1th test, It represents the interval between the k-1th test and the next test of the i-th patient, It represents the interval between the kth test of the i-th patient and the next test.

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