Colorectal cancer liver metastasis risk screening and evaluating method and system

Abstract

The invention discloses a colorectal cancer liver metastasis risk screening and evaluating method and system in the technical field of medical systems. The system comprises an acquisition module, a serum measurement and calculation module, a peripheral blood circulating tumor cell measurement and calculation module, a molecular marker measurement and calculation module, a probability classification module, a probability judgment module and an evaluation result output module. The probability judgment module is used for evaluating whether the liver metastasis condition exists in the body of the patient or not, and the evaluation content comprises the following steps; when X is equal to P, the serum tumor marker accounts for 5%, and the addition amount of X is greater than P0.1 by 2%, so that the addition amount is increased to 20%; when peripheral blood circulating tumor cells are detected in the blood of the patient, 60% is calculated; when Y is equal to P1, the molecular marker accounts for 5%, and Y is greater than P1 0.1 and is added by 2%, so that Y is increased to 20%. The method is simple and easy to operate, and the probability value of liver metastasis is accurately judged by combining data information of serum tumor markers, peripheral blood circulating tumor cells and molecular markers.

Description

Technical Field

[0001] This invention belongs to the field of medical system technology, specifically a method and system for screening and assessing the risk of colorectal cancer liver metastasis. Background Technology

[0002] The incidence of colorectal cancer has been rising significantly in recent years, making it one of the most common malignant tumors worldwide. In the United States, colorectal cancer ranks second among men and third among women in terms of cancer incidence. Approximately 35%-55% of patients with malignant colorectal cancer develop liver metastases during malignant progression, which is one of the leading causes of death in colorectal cancer patients. Therefore, early diagnosis of liver metastases in colorectal cancer and selection of appropriate treatment methods are crucial for improving patient survival rates. Colorectal cancer liver metastasis is a complex process, roughly involving steps such as cancer cells invading blood vessels from the primary site in the colorectal region, circulating tumor cells evading the body's immune system and invading the liver, and the formation of new blood vessels.

[0003] The aforementioned process involves numerous genetic and molecular changes. Detecting these substances can help assess the risk of colorectal cancer liver metastasis at an early stage. However, due to the liver's filtration and immune clearance functions, the number of CTCs in the peripheral blood of colorectal cancer patients is extremely low, and the currently discovered markers are not highly specific, only indicating a certain risk of tumor metastasis to some extent, but not confirming whether it is liver metastasis. Further research is needed to explore specific markers. Therefore, it is necessary to propose a method and system for screening and assessing the risk of colorectal cancer liver metastasis that combines multiple detection data. Summary of the Invention

[0004] To address the problem that a single data point is insufficient for accurately determining whether liver metastasis has occurred, the present invention aims to provide a method and system for screening and assessing the risk of colorectal cancer liver metastasis by combining multiple detection data. By combining serum tumor markers, peripheral blood circulating tumor cells, and molecular marker data, the probability of liver metastasis can be determined more accurately.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: a colorectal cancer liver metastasis risk screening and assessment system, comprising a data acquisition module, a serum calculation module, a peripheral blood circulating tumor cell calculation module, a molecular marker calculation module, a probability classification module, and a probability judgment module;

[0006] The acquisition module is used to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular markers from patients and transmit them to the next level.

[0007] The serum measurement module is used to collect serum tumor marker values ​​from patients, perform statistical calculations on all values ​​to obtain a probability value X, set a threshold P, compare X with P, and transmit the result to the next level.

[0008] The peripheral blood circulating tumor cell measurement module is used to statistically analyze the content of peripheral blood circulating tumor cells in the collected patient's blood and calculate the proportion of peripheral blood circulating tumor cells to determine whether cancer cells have metastasized to the liver.

[0009] The molecular marker calculation module receives the calculated molecular expressions in the patient's body, including MINT1, MINT2, MINT31, MLH1, and p16, and detects the methylation status of MINT1, MINT2, MINT31, MLH1, and p16. It then uses statistical methods to calculate the results and obtain a probability value Y, and sets a threshold P. 1 ,Y and P 1 Perform size comparison and send the results to the next level;

[0010] The probability classification module is used to design the weighted proportions of serum tumor markers, peripheral blood circulating tumor cells, and molecular markers, with peripheral blood circulating tumor cells accounting for 60% of the calculated values, and serum tumor markers and molecular markers each accounting for 20%.

[0011] The probability judgment module is used to receive the calculated probability values ​​of serum tumor markers, peripheral blood circulating tumor cells and molecular markers, and combine them with the calculated values ​​from the time-weighted module to assess whether there is liver metastasis in the patient. The assessment includes the following:

[0012] When X = P, serum tumor markers are counted at 5%, and for every 0.1 greater than P, an additional 2% is added, increasing incrementally up to 20%; when peripheral blood circulating tumor cells are detected in the patient's blood, the count is 60%; when Y = P 1 Molecular markers account for 5%, and Y is greater than P. 1 A 2% bonus is added for every 0.1, increasing incrementally up to 20%.

[0013] The basic protocol works as follows: When assessing the risk of colorectal cancer liver metastasis in patients, the patient first needs to undergo testing for serum tumor markers, peripheral blood circulating tumor cells, and molecular markers. Then, the data acquisition module collects the relevant test information. Simultaneously, after the information is collected, the serum calculation module, the peripheral blood circulating tumor cell calculation module, and the molecular marker calculation module perform corresponding data statistical calculations to obtain the corresponding values. The probability classification module performs probability weighting and classification on the calculated values, and the probability judgment module makes probability judgments based on the weighted values. The presence or absence of peripheral blood circulating tumor cells accounts for a large probability weight. Finally, the corresponding liver metastasis probability value is output.

[0014] The beneficial effects of the basic approach are: 1. Compared with existing technologies that only assess the liver metastasis of patients based on a single indicator, this invention, by combining serum tumor markers, peripheral blood circulating tumor cells, and molecular markers, can monitor the changes in various indicators of the body during liver metastasis from multiple angles and with multiple data points, thereby calculating a more accurate probability value of liver metastasis.

[0015] 2. When classifying serum tumor markers, peripheral blood circulating tumor cells (PBMCs), and molecular markers probabilistically, PBMCs account for a relatively high proportion. This is because PBMCs represent an intermediate state of tumor cells migrating from the primary site to metastatic lesions. Detecting surface markers on PBMCs can predict whether they have metastasized to the liver, thus achieving early diagnosis. Furthermore, PBMCs are among the few cells that can evade the body's immune response and survive. They are a small number of cells in tumor tissue that possess self-renewal, multi-differentiation potential, and unlimited proliferative capacity. PBMCs can evade threats and successfully reach distant organs.

[0016] Meanwhile, due to the liver's filtration and immune clearance functions, the number of CTCs in the peripheral blood of colorectal cancer patients is extremely low. Therefore, serum tumor markers and molecular markers are needed to assist in detection. Among them, by analyzing the methylation status of MINT1, MINT2, MINT31, MLH1, and p16 in colorectal cancer patients, it was found that these gene methylation changes occur before liver metastasis of colorectal cancer, suggesting that they may promote liver metastasis of colorectal cancer. Furthermore, when performing serum tumor marker detection, the serum CEA and CA19-9 levels of colorectal cancer patients with liver metastasis are significantly higher than those of patients without metastasis. Therefore, it can supplement and assist in the judgment of whether the patient has developed liver metastasis. In this way, by combining multiple data, a more comprehensive judgment of liver metastasis can be achieved.

[0017] 3. When making probability judgments, by continuously increasing the probability value based on the calculated values ​​of different situations, it is possible to accurately represent the different situations calculated for different patients, thereby providing a more accurate and scientific display of the probability of liver metastasis for different patients, and enabling doctors to make more objective judgments.

[0018] Furthermore, it also includes a time-weighted module, which is used to re-examine the patient's serum tumor markers, peripheral blood circulating tumor cells, and molecular markers every month, in order to calculate the probability values ​​of serum tumor markers, peripheral blood circulating tumor cells, and molecular markers in the patient's body at different time periods.

[0019] The benefits of the basic protocol are: regular follow-up examinations can more accurately monitor changes in serum tumor markers, peripheral blood circulating tumor cells, and molecular markers in patients, thereby more accurately assessing the risk of liver metastasis; and the follow-up results from the time-weighted module can guide doctors to develop more precise treatment plans and provide patients with more personalized treatment.

[0020] Furthermore, when collecting serum tumor markers in the acquisition module, the collected values ​​include CEA, CA19-9, CA50, and CA242.

[0021] The beneficial effects of the basic protocol are: 1. By simultaneously detecting multiple serum tumor markers, the risk of colorectal cancer liver metastasis can be assessed more comprehensively. Different markers change differently during tumor occurrence and development; combined detection can more accurately reflect the tumor status; combined detection of multiple markers can improve detection accuracy. Different markers differ in terms of detection specificity and sensitivity; by combining the results of multiple markers, they can complement each other, reducing the possibility of misdiagnosis and missed diagnosis.

[0022] Furthermore, when performing statistical calculations, the serum measurement module first selects independent variables stepwise based on the forward method of partial maximum likelihood estimation to conduct multi-factor stepwise regression analysis, and selects CEA, CA19-9, CA50 and CA242 into the regression equation, as shown in the following formula;

[0023]

[0024] Where CEA, CA19-9, CA50 and CA242 are X1, X2, X3 and X4 respectively, and 0.439 is a constant;

[0025] Furthermore, the closer the Q value is to 1, the greater the likelihood of metastasis in the patient; the closer the Q value is to 0, the less likely the patient is to metastasize, and P is 0.4.

[0026] The beneficial effects of the basic approach are: by using a forward approach based on partial maximum likelihood estimation for multivariate stepwise regression analysis, the serum measurement module can more accurately predict the risk of colorectal cancer liver metastasis, providing patients with more personalized treatment plans. Simultaneously, this method can also provide valuable reference information for clinicians and researchers, contributing to a deeper understanding of the mechanisms of colorectal cancer liver metastasis and the development of more effective treatment strategies.

[0027] Furthermore, the molecular marker calculation module calculates molecular markers significant for liver metastasis using stepwise logistic regression analysis, and uses t-tests for intergroup difference analysis; categorical data are expressed as n, and intergroup difference analysis uses χ². 2For analysis of differences in ordinal data, the rank-sum test is used; and the closer the Y value is to 1, the greater the likelihood of metastasis in the patient, and the closer the Y value is to 0, the less likely the patient is to have metastasis. P0 1 It is 0.4.

[0028] Furthermore, the calculation formula for the time-weighted module is as follows;

[0029]

[0030] P represents the time-series predicted values ​​of tumor markers, circulating tumor cells, or molecular markers in peripheral blood; m represents the number of data items; and w1, w2, w3, and w m For the corresponding weights, ρ i-1 ρ i-2 ρ i-3 and ρ i-m For the corresponding data items.

[0031] The beneficial effect of the basic scheme is that by scientifically measuring the time series, the data can be made more objective, thereby making the subsequent output probability values ​​more comprehensive.

[0032] Furthermore, the data collection module also includes personal vital signs and lifestyle habits units;

[0033] The personal vital signs unit is used to collect patients' personal physical information, including age, gender, genetic history, history of liver disease, diabetes, history of cardiovascular disease, and body mass index;

[0034] The lifestyle unit is used to collect information on patients' lifestyle habits, including long-term consumption of high-fat, high-calorie, high-protein, and low-fiber foods, smoking history, frequency of alcohol consumption, exercise habits, and bowel habits.

[0035] The beneficial effects of the basic protocol are: the inclusion of personal physical characteristics and lifestyle components allows for a more comprehensive assessment of risk factors for colorectal cancer liver metastasis. Personal physical condition and lifestyle information can both influence a patient's health status and disease progression. By comprehensively considering these factors, a more accurate assessment of the patient's risk of liver metastasis can be achieved.

[0036] The intestines come into direct contact with food, and the composition of food can directly affect the homeostasis and function of the intestinal environment. Long-term unhealthy dietary habits of consuming high-fat, high-calorie, high-protein, and low-fiber foods may potentially lead to colorectal cancer. The main carcinogen in cigarettes is benzo[a]pyrene, which can increase the level of reactive oxygen species and the activity of phospholipase A2, and induce the proliferation of rectal cancer HT-29 and HCT-15 cell lines. Nicotine, the main alkaloid in tobacco, not only promotes the proliferation of colon cancer cell lines but also enhances their resistance to cytotoxic drugs. Ethanol, once absorbed into the body, can be converted into acetaldehyde, which can cause interstrand cross-linking of DNA, producing a strong carcinogenic effect. Ethanol can chemically couple with membrane phospholipids, leading to the translocation of β-linkin and atresia-1-related nucleic acid-binding protein genes involved in cancer cell proliferation and invasion. It also leads to the expression of cytochrome P4502E1, inhibits the expression of antioxidants and cell-protective enzymes, directly damages DNA, and participates in inflammation and tumor metastasis. Furthermore, long-term alcohol abuse can lead to folic acid deficiency and abnormal DNA methylation.

[0037] Furthermore, in both the personal vital signs unit and the lifestyle unit, the doctor's experience is used to judge the patient's personal vital signs and lifestyle habits to conduct probability value assessment. The maximum value for both the personal vital signs unit and the lifestyle unit is 10%.

[0038] After the individual vital signs unit and the lifestyle habit unit record the probability values ​​of the patient's corresponding individual vital signs and lifestyle habits, these probability values ​​are transmitted to the probability judgment module.

[0039] The beneficial effect of the basic protocol is that the information collected from the individual physical characteristics unit and the lifestyle unit can complement each other, improving the accuracy of the assessment. For example, personal physical information such as age, gender, genetic history, history of liver disease, diabetes, and history of cardiovascular disease can be combined with information on serum tumor markers, peripheral blood circulating tumor cells, and molecular markers to jointly predict the risk of liver metastasis.

[0040] Furthermore, a method for screening and assessing the risk of colorectal cancer liver metastasis includes the following steps:

[0041] Step 1, Data Collection: Use the data collection module to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular marker detection information respectively;

[0042] Step 2, Value Calculation: The serum tumor marker, peripheral blood circulating tumor cell, and molecular marker detection were statistically calculated using the serum calculation module, peripheral blood circulating tumor cell, and molecular marker modules respectively, and the corresponding calculated values ​​were obtained.

[0043] Step 3, Time-series weighting: The time-series weighting module is used to perform time-series weighted calculations on the values ​​calculated in Step 2, so as to statistically analyze and predict the values ​​at each stage;

[0044] Step 4, Probability Judgment: The probability classification module and probability judgment module are used to perform probability calculation on the values ​​obtained in Step 3, thereby obtaining the corresponding liver metastasis probability value for the patient.

[0045] Step 5, Probability Correction: Using the individual physical signs unit and lifestyle habit unit, the patient's personal physical condition information and lifestyle habit information are combined with the probability value calculated in Step 4, and then the corresponding liver metastasis probability value is obtained according to the patient's actual situation.

[0046] The beneficial effects of the basic protocol are: this method not only considers biomarkers such as tumor markers, but also incorporates the patient's personal physical condition and lifestyle information, thus providing a more comprehensive assessment of liver metastasis risk. The inclusion of personal physical characteristics and lifestyle components allows for the integrated consideration of multiple risk factors, improving the accuracy of the assessment. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the colorectal cancer liver metastasis risk screening and assessment system in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the method for screening and assessing the risk of colorectal cancer liver metastasis in an embodiment of the present invention. Detailed Implementation

[0049] The following detailed description illustrates the specific implementation method:

[0050] Example 1

[0051] The basics are as follows: Figure 1 As shown: A colorectal cancer liver metastasis risk screening and assessment system includes a data collection module, a serum calculation module, a peripheral blood circulating tumor cell calculation module, a molecular marker calculation module, a probability classification module, a probability judgment module, and an assessment result output module;

[0052] The acquisition module is used to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular markers from patients and transmit them to the next level. When collecting serum tumor markers, the acquisition module collects values ​​including CEA, CA19-9, CA50, and CA242.

[0053] The serum measurement module is used to collect serum tumor marker values ​​from patients, perform statistical calculations on all values ​​to obtain a probability value X, set a threshold P, compare X with P, and transmit the result to the next level.

[0054] When performing statistical calculations, the serum measurement module first selects independent variables stepwise based on the forward method of partial maximum likelihood estimation to conduct multi-factor stepwise regression analysis, and selects CEA, CA19-9, CA50 and CA242 into the regression equation, as shown in the following formula;

[0055]

[0056] Where CEA, CA19-9, CA50 and CA242 are X1, X2, X3 and X4 respectively, and 0.439 is a constant;

[0057] Furthermore, the closer the Q value is to 1, the greater the likelihood of metastasis in the patient; the closer the Q value is to 0, the less likely the patient is to metastasize, and P is 0.4.

[0058] The peripheral blood circulating tumor cell measurement module is used to statistically analyze the content of peripheral blood circulating tumor cells in the collected patient's blood and calculate the proportion of peripheral blood circulating tumor cells to determine whether cancer cells have metastasized to the liver.

[0059] The molecular marker calculation module receives the calculated molecular expressions in the patient's body, including MINT1, MINT2, MINT31, MLH1, and p16, and detects the methylation status of MINT1, MINT2, MINT31, MLH1, and p16. It then uses statistical methods to calculate the results and obtain a probability value Y, and sets a threshold P. 1 ,Y and P 1 Perform size comparison and send the results to the next level;

[0060] The molecular marker measurement module calculates molecular markers significant for liver metastasis using stepwise logistic regression analysis, and performs t-tests for intergroup differences. Count data are expressed as n, and χ² tests are used for intergroup differences. 2 For analysis of differences in ordinal data, the rank-sum test is used; and the closer the Y value is to 1, the greater the likelihood of metastasis in the patient, and the closer the Y value is to 0, the less likely the patient is to have metastasis. P0 1 It is 0.4.

[0061] The probability classification module is used to design the weighted proportions of serum tumor markers, peripheral blood circulating tumor cells, and molecular markers, with peripheral blood circulating tumor cells accounting for 60% of the calculated values, and serum tumor markers and molecular markers each accounting for 20%.

[0062] The probability judgment module is used to receive the calculated probability values ​​of serum tumor markers, peripheral blood circulating tumor cells and molecular markers, and combine them with the calculated values ​​from the time-weighted module to assess whether there is liver metastasis in the patient. The assessment includes the following:

[0063] When X = P, serum tumor markers are counted at 5%, and for every 0.1 greater than P, an additional 2% is added, increasing incrementally up to 20%; when peripheral blood circulating tumor cells are detected in the patient's blood, the count is 60%; when Y = P 1 Molecular markers account for 5%, and Y is greater than P. 1 A 2% bonus is added for every 0.1, increasing incrementally up to 20%.

[0064] The specific implementation process is as follows: When assessing liver metastasis, the acquisition module first collects information on serum tumor markers, peripheral blood circulating tumor cells, and molecular markers. Simultaneously, the serum calculation module and the molecular marker calculation module statistically analyze the detected information and derive the corresponding statistical values. The peripheral blood circulating tumor cell calculation module directly records whether the corresponding peripheral blood circulating tumor cells are detected in the patient's blood. The probability classification module assigns probability weights to each value, and the probability judgment module evaluates the probability value of the calculated value according to the corresponding weight. Finally, the corresponding liver metastasis probability value of the patient is output for doctors to use as a more objective reference.

[0065] In actual probability assessment, for example, if the patient's peripheral blood circulating tumor cell detection module records the presence of this cell in their blood, the probability value is calculated as 60%. Meanwhile, in the serum detection module, the X value is 0.7. Since P is 0.4, X > P, and 0.7 - 0.4 = 0.3. According to the probability judgment module's calculation logic, because X is greater than P 0.3, the probability of metastasis in serum detection is calculated as 11%. Furthermore, if the Y value calculated by the molecular marker detection module is 0.8, according to the calculation method in the serum detection module, the probability in molecular marker detection is calculated as 13%. Therefore, the final total probability value is 60% + 11% + 13% = 84%.

[0066] Example 2

[0067] The difference from the above embodiments is that it also includes a time-weighted module, which is used to re-examine the patient's serum tumor markers, peripheral blood circulating tumor cells and molecular markers every month, so as to calculate the probability values ​​of serum tumor markers, peripheral blood circulating tumor cells and molecular markers in the patient's body at different time periods.

[0068] The calculation formula for the time-weighted module is as follows;

[0069]

[0070] P represents the time-series predicted values ​​of tumor markers, circulating tumor cells, or molecular markers in peripheral blood; m represents the number of data items; and w1, w2, w3, and w m For the corresponding weights, ρ i-1 ρi-2 ρ i-3 and ρ i-m For the corresponding data items.

[0071] The specific implementation process is as follows: Based on the probability values ​​calculated using time-weighted methods, doctors can more accurately assess the patient's condition and develop more personalized treatment plans, thereby improving treatment effectiveness and patient survival rates.

[0072] Example 3

[0073] The difference from the above embodiments is that the data acquisition module also includes a personal vital signs unit and a living habits unit;

[0074] The personal vital signs unit is used to collect patients' personal physical information, including age, gender, genetic history, history of liver disease, diabetes, history of cardiovascular disease, and body mass index;

[0075] The lifestyle unit is used to collect information on patients' lifestyle habits, including long-term consumption of high-fat, high-calorie, high-protein, and low-fiber foods, smoking history, frequency of alcohol consumption, exercise habits, and bowel habits.

[0076] Furthermore, both the personal vital signs unit and the lifestyle unit assess the patient's personal vital signs and lifestyle based on the doctor's experience, and evaluate the probability value. The maximum value for both the personal vital signs unit and the lifestyle unit is 10%.

[0077] After the individual vital signs unit and the lifestyle habit unit record the probability values ​​of the patient's corresponding individual vital signs and lifestyle habits, these probability values ​​are transmitted to the probability judgment module.

[0078] The specific implementation process is as follows: Information collected from the individual vital signs unit and lifestyle unit can help doctors develop more personalized treatment plans and preventive measures. For example, if a patient has high-risk factors such as a family history of genetic disorders or a long-term diet high in fat, the doctor can recommend that they undergo closer monitoring or take specific preventive measures.

[0079] Example 4

[0080] The difference from the above embodiments is that, as Figure 2 As shown, a method for screening and assessing the risk of colorectal cancer liver metastasis includes the following steps:

[0081] Step 1, Data Collection: Use the data collection module to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular marker detection information respectively;

[0082] Step 2, Value Calculation: The serum tumor marker, peripheral blood circulating tumor cell, and molecular marker detection were statistically calculated using the serum calculation module, peripheral blood circulating tumor cell, and molecular marker modules respectively, and the corresponding calculated values ​​were obtained.

[0083] Step 3, Time-series weighting: The time-series weighting module is used to perform time-series weighted calculations on the values ​​calculated in Step 2, so as to statistically analyze and predict the values ​​at each stage;

[0084] Step 4, Probability Judgment: The probability classification module and probability judgment module are used to perform probability calculation on the values ​​obtained in Step 3, thereby obtaining the corresponding liver metastasis probability value for the patient.

[0085] Step 5, Probability Correction: Using the individual physical signs unit and lifestyle habit unit, the patient's personal physical condition information and lifestyle habit information are combined with the probability value calculated in Step 4, and then the corresponding liver metastasis probability value is obtained according to the patient's actual situation.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A colorectal cancer liver metastasis risk screening and assessment system, characterized in that: It includes a data acquisition module, a serum calculation module, a peripheral blood circulating tumor cell calculation module, a molecular marker calculation module, a probability classification module, a probability judgment module, and an evaluation result output module; The acquisition module is used to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular markers from patients and transmit them to the next level. The serum measurement module is used to collect serum tumor marker values ​​from patients, perform statistical calculations on all values ​​to obtain a probability value X, set a threshold P, compare X with P, and transmit the result to the next level. The peripheral blood circulating tumor cell measurement module is used to statistically analyze the content of peripheral blood circulating tumor cells in the collected patient's blood and calculate the proportion of peripheral blood circulating tumor cells to determine whether cancer cells have metastasized to the liver. The molecular marker calculation module receives the calculated molecular expressions in the patient's body, including MINT1, MINT2, MINT31, MLH1, and p16, and detects the methylation status of MINT1, MINT2, MINT31, MLH1, and p16. It then uses statistical methods to calculate the results and obtain a probability value Y, and sets a threshold P. 1 ,Y and P 1 Perform size comparison and send the results to the next level; The probability classification module is used to design the weighted proportions of serum tumor markers, peripheral blood circulating tumor cells, and molecular markers, with peripheral blood circulating tumor cells accounting for 60% of the calculated values, and serum tumor markers and molecular markers each accounting for 20%. The probability judgment module is used to receive the calculated probability values ​​of serum tumor markers, peripheral blood circulating tumor cells and molecular markers, and combine them with the calculated values ​​from the time-weighted module to assess whether there is liver metastasis in the patient. The assessment includes the following: When X = P, serum tumor markers are counted at 5%, and for every 0.1 greater than P, an additional 2% is added, increasing incrementally up to 20%; when peripheral blood circulating tumor cells are detected in the patient's blood, the count is 60%; when Y = P 1 Molecular markers account for 5%, and Y is greater than P. 1 A 2% bonus is added for every 0.1, increasing incrementally up to 20%.

2. The colorectal cancer liver metastasis risk screening and assessment system according to claim 1, characterized in that: It also includes a time-weighted module, which is used to re-examine the patient's serum tumor markers, peripheral blood circulating tumor cells and molecular markers every month, in order to calculate the probability values ​​of serum tumor markers, peripheral blood circulating tumor cells and molecular markers in the patient's body at different time periods.

3. The colorectal cancer liver metastasis risk screening and assessment system according to claim 2, characterized in that: When collecting serum tumor markers in the acquisition module, the collected values ​​include CEA, CA19-9, CA50, and CA242.

4. The colorectal cancer liver metastasis risk screening and assessment system according to claim 3, characterized in that: When performing statistical calculations, the serum measurement module first selects independent variables stepwise based on the forward method of partial maximum likelihood estimation for multivariate stepwise regression analysis, and selects CEA, CA19-9, CA50 and CA242 into the regression equation, as shown in the following formula; Where CEA, CA19-9, CA50 and CA242 are X1, X2, X3 and X4 respectively, and 0.439 is a constant; Furthermore, the closer the Q value is to 1, the greater the likelihood of metastasis in the patient; the closer the Q value is to 0, the less likely the patient is to metastasize, and P is 0.

4.

5. The colorectal cancer liver metastasis risk screening and assessment system according to claim 4, characterized in that: The molecular biomarker calculation module performs the following calculations: stepwise logistic regression analysis is used to identify molecular biomarkers significant for liver metastasis; t-tests are used for intergroup difference analysis; categorical data are expressed as n, and χ² tests are used for intergroup difference analysis. 2 For analysis of differences in ordinal data, the rank-sum test is used; and the closer the Y value is to 1, the greater the likelihood of metastasis in the patient, and the closer the Y value is to 0, the less likely the patient is to have metastasis. P0 1 It is 0.

4.

6. The colorectal cancer liver metastasis risk screening and assessment system according to claim 5, characterized in that: The calculation formula for the time-weighted module is as follows; P represents the time-series predicted values ​​of tumor markers, circulating tumor cells, or molecular markers in peripheral blood; m represents the number of data items; and w1, w2, w3, and w m For the corresponding weights, ρ i-1 ρ i-2 ρ i-3 and ρ i-m For the corresponding data items.

7. The colorectal cancer liver metastasis risk screening and assessment system according to claim 6, characterized in that: The data collection module also includes personal vital signs and lifestyle habits units; The personal vital signs unit is used to collect patients' personal physical information, including age, gender, genetic history, history of liver disease, diabetes, history of cardiovascular disease, and body mass index; The lifestyle unit is used to collect information on patients' lifestyle habits, including long-term consumption of high-fat, high-calorie, high-protein, and low-fiber foods, smoking history, frequency of alcohol consumption, exercise habits, and bowel habits.

8. The colorectal cancer liver metastasis risk screening and assessment system according to claim 7, characterized in that: Both the personal vital signs unit and the lifestyle unit assess the patient's personal vital signs and lifestyle based on the doctor's experience, and evaluate the probability value. The maximum value for both the personal vital signs unit and the lifestyle unit is 10%. After the individual vital signs unit and the lifestyle habit unit record the probability values ​​of the patient's corresponding individual vital signs and lifestyle habits, these probability values ​​are transmitted to the probability judgment module.

9. A method for screening and assessing the risk of colorectal cancer liver metastasis, characterized in that: Includes the following steps: Step 1, Data Collection: Use the data collection module to collect serum tumor markers, peripheral blood circulating tumor cells, and molecular marker detection information respectively; Step 2, Value Calculation: The serum tumor marker, peripheral blood circulating tumor cell, and molecular marker detection were statistically calculated using the serum calculation module, peripheral blood circulating tumor cell, and molecular marker modules respectively, and the corresponding calculated values ​​were obtained. Step 3, Time-series weighting: The time-series weighting module is used to perform time-series weighted calculations on the values ​​calculated in Step 2, so as to statistically analyze and predict the values ​​at each stage; Step 4, Probability Judgment: The probability classification module and probability judgment module are used to perform probability calculation on the values ​​obtained in Step 3, thereby obtaining the corresponding liver metastasis probability value for the patient. Step 5, Probability Correction: Using the individual physical signs unit and lifestyle habit unit, the patient's personal physical condition information and lifestyle habit information are combined with the probability value calculated in Step 4, and then the corresponding liver metastasis probability value is obtained according to the patient's actual situation.

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