Method and system for evaluating postoperative recurrence risk of liver cancer based on metabonomics analysis

Abstract

The invention relates to the technical field of clinical diagnosis and prediction, in particular to a liver cancer postoperative recurrence risk assessment method and system based on metabonomics analys.The liver cancer postoperative recurrence risk assessment method comprises the steps that a urine analyzer, an automatic biochemical analyzer and a CT scanner are obtained, a target patient is confirmed, and the patient ID of the target patient is obtained; the method comprises the following steps: reading patient portrait information from a pre-constructed hospital database based on a patient ID, obtaining historical detection data of a target patient, calculating a comprehensive health behavior index according to a smoking influence index and a drug comprehensive value, obtaining a metabolite risk value based on the historical detection data, a urine analyzer and an automatic biochemical analyzer, and determining the risk value of the metabolite by using a postoperative liver image. The focus severity is calculated, the recurrence risk value is calculated according to the comprehensive health behavior index, the metabolite risk value and the focus severity, and evaluation of the postoperative recurrence risk of the target patient is completed. The accuracy and the automation degree of evaluating the postoperative recurrence risk of the liver cancer patient can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical diagnosis and prediction, and in particular to a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis. Background Art

[0002] With the continuous development of modern medical technology, risk assessment systems have been widely used in clinical diagnosis, treatment decision-making, and patient management. Among them, the liver cancer postoperative recurrence risk assessment system is often used to predict the risk of recurrence after surgery.

[0003] Currently, doctors mainly assess the risk of recurrence of liver cancer after surgery based on traditional clinical indicators, or make a preliminary judgment on the risk of recurrence by setting a single prognostic indicator threshold.

[0004] While these methods can assess the risk of liver cancer recurrence after surgery to a certain extent, they fail to fully consider the relationship between individual metabolic characteristics and pathophysiological states, and are easily influenced by the physician's personal medical experience. This results in insufficient prediction accuracy when assessing the risk of liver cancer recurrence after surgery. Therefore, improving the accuracy and automation of assessing the risk of liver cancer recurrence after surgery has become an urgent issue. Summary of the Invention

[0005] The present invention provides a method and system for assessing the risk of postoperative recurrence of liver cancer based on metabolomics analysis, and a computer-readable storage medium, the main purpose of which is to improve the accuracy and automation of assessing the risk of postoperative recurrence of liver cancer patients.

[0006] To achieve the above objectives, the present invention provides a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis, comprising:

[0007] Obtain urine analyzers, automated biochemical analyzers, and CT scanners;

[0008] Identify the target patient, obtain the patient ID of the target patient, and read the patient profile information from the pre-built hospital database based on the patient ID. The patient profile information includes: patient age, body mass index, and lifestyle. The lifestyle information includes: drinking frequency, average number of cigarettes smoked per day, smoking duration, exercise frequency, frequency of staying up late, and multiple medication data. The medication data includes: dosage and duration of use.

[0009] Obtaining historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration;

[0010] Preprocess the patient portrait information to obtain the smoking impact index and drug comprehensive value, and calculate the comprehensive health behavior index based on the smoking impact index and drug comprehensive value;

[0011] Obtain metabolite risk values ​​based on historical test data, urine analyzers, and automated biochemical analyzers;

[0012] Using a CT scanner to obtain postoperative liver images of the target patient, and using the postoperative liver images to calculate the severity of the lesion;

[0013] The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient.

[0014] Optionally, the pre-processing of the patient portrait information to obtain the smoking impact index and the comprehensive drug value includes:

[0015] The smoking impact index is calculated based on the average number of cigarettes smoked per day and the length of smoking history in the patient profile information. The calculation formula is as follows:

[0016]

[0017] Wherein, A represents the smoking impact index, B represents the average number of cigarettes smoked per day, N represents the smoking age, α1 and α2 are the preset first impact coefficient and the preset second impact coefficient respectively, and ln is the natural logarithm;

[0018] Confirm the age correction value based on the patient's age in the patient portrait information;

[0019] The comprehensive drug value is calculated based on multiple medication data and age correction values ​​in the patient portrait information. The calculation formula is as follows:

[0020]

[0021] Among them, M represents the comprehensive value of the drug, n represents the number of drug data in multiple drug data, J i represents the dosage of the i-th medication data in multiple medication data, T i A represents the taking period of the i-th medication data in multiple medication data. adj Indicates the age correction value.

[0022] Optionally, the calculation of the comprehensive health behavior index based on the smoking impact index and the comprehensive drug value includes:

[0023] The comprehensive health behavior index is calculated based on the smoking impact index, drinking frequency, staying up late frequency, exercise frequency, and drug comprehensive value. The calculation formula is as follows:

[0024]

[0025] Among them, Z represents the comprehensive health behavior index, C represents the frequency of drinking, A represents the smoking impact index, S represents the frequency of exercise, Y represents the frequency of staying up late, M represents the comprehensive value of drugs, tanh represents the hyperbolic tangent function, and log represents the logarithmic function.

[0026] Optionally, obtaining metabolite risk values ​​based on historical test data, a urine analyzer, and an automatic biochemical analyzer includes:

[0027] Collecting a first sample from a target patient at a preset first collection time, wherein the first sample includes: first venous blood and first urine;

[0028] Using a urine analyzer to test the first urine to obtain a first urobilinogen concentration and a first urine protein concentration;

[0029] Separating the first venous blood to obtain a first serum and a first plasma;

[0030] The first serum and the first plasma are analyzed respectively by an automatic biochemical analyzer to obtain a first alpha-embryon concentration and a first choline concentration;

[0031] Calculating a urobilin fluctuation value based on the first urobilin concentration and the initial urobilin concentration in the historical test data;

[0032] Comparing urobilinogen fluctuation values ​​with pre-set fluctuation thresholds;

[0033] If the urobilin fluctuation value is less than or equal to the fluctuation threshold, the first urobilin concentration is used as the first target urobilin concentration;

[0034] If the urobilinogen fluctuation value is greater than the fluctuation threshold, a first target urobilinogen concentration is calculated based on the first urobilinogen concentration and the initial urobilinogen concentration, wherein the first target urobilinogen concentration is an average of the first urobilinogen concentration and the initial urobilinogen concentration;

[0035] Obtaining a first target urine protein concentration based on the fluctuation threshold, the first urine protein concentration, and the initial urine protein concentration; obtaining a first target alpha-fetoprotein concentration based on the fluctuation threshold, the first alpha-fetoprotein concentration, and the initial alpha-fetoprotein concentration; and obtaining a first target choline concentration based on the fluctuation threshold, the first choline concentration, and the initial choline concentration;

[0036] Calculating a first metabolite risk value according to a first target urobilinogen concentration, a first target urinary protein concentration, a first target alpha-embryonin concentration, and a first target choline concentration;

[0037] Obtaining a second metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset second collection time; obtaining a third metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset third collection time;

[0038] A metabolite risk value is calculated according to the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value, wherein the metabolite risk value is an average of the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value.

[0039] Optionally, the calculation formula of the urobilinogen fluctuation value is as follows:

[0040]

[0041] Among them, Δ URO is the fluctuation value of urobilinogen, [URO x ] and [URO0] are the first urobilinogen concentration and the initial urobilinogen concentration, respectively, and | | refers to the absolute value.

[0042] Optionally, the calculation formula of the first metabolite risk value is as follows:

[0043] R d =β1×e ([URO]×[PRO]) +β2×ln(1+[AFP])(1+[Chol])

[0044] Among them, R d represents the first metabolite risk value, [URO] represents the first target urobilinogen concentration, [PRO] represents the first target urinary protein concentration, [AFP] represents the first target alpha-fetoprotein concentration, [Chol] represents the first target choline concentration, β1 and β2 are the preset first coefficient and the preset second coefficient, respectively, and e is a natural constant.

[0045] Optionally, the step of obtaining a postoperative liver image of the target patient using a CT scanner includes:

[0046] Confirm the fixed patient based on the CT scanner and the target patient;

[0047] Confirm radiation dose and tube voltage based on body mass index;

[0048] The radiation dose and tube voltage are input into the CT scanner to obtain a target CT scanner;

[0049] The target CT scanner is started, and a postoperative liver image of the fixed patient is acquired using the started target CT scanner.

[0050] Optionally, the calculation of lesion severity using postoperative liver images includes:

[0051] Obtain preoperative liver images of target patients and liver images of healthy subjects;

[0052] Inputting the preoperative liver image and the postoperative liver image into a pre-built image processing model to obtain a first analysis model, and obtaining a first difference degree using the first analysis model;

[0053] Obtaining a second difference based on liver images of healthy people, liver images after surgery, and an image processing model;

[0054] The severity of the lesion is calculated using the first difference and the second difference. The calculation formula is as follows:

[0055]

[0056] Where χ represents the severity of the lesion, ε1 represents the first difference, and ε2 represents the second difference.

[0057] Optionally, the calculation of the recurrence risk value based on the comprehensive health behavior index, metabolite risk value and lesion severity includes:

[0058] The recurrence risk value was calculated using the comprehensive health behavior index, metabolite risk value, and lesion severity. The calculation formula is as follows:

[0059]

[0060] Among them, RS represents the recurrence risk value, Z0 is the preset mean of health behavior index, R dx represents the metabolite risk value, γ1, γ2 and γ3 are the preset first risk coefficient, the preset second risk coefficient and the preset third risk coefficient respectively.

[0061] To achieve the above objectives, the present invention further provides a system for assessing the risk of liver cancer recurrence after surgery based on metabolomics analysis, comprising:

[0062] an initial instrument preparation module for acquiring a urine analyzer, an automated biochemistry analyzer, and a CT scanner;

[0063] The health value confirmation module is used to confirm the target patient, obtain the patient ID of the target patient, and read the patient portrait information from the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data includes: dosage and medication cycle, and obtain the historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration and initial choline concentration;

[0064] The patient information processing module is used to pre-process patient portrait information to obtain a smoking impact index and a comprehensive medication value. Based on the smoking impact index and the comprehensive medication value, a comprehensive health behavior index is calculated. Metabolite risk values ​​are obtained based on historical test data, a urine analyzer, and an automatic biochemical analyzer. Postoperative liver images of target patients are obtained using a CT scanner and the lesion severity is calculated using the postoperative liver images.

[0065] The patient risk assessment module is used to calculate the recurrence risk value based on the comprehensive health behavior index, metabolite risk value and lesion severity, and complete the assessment of the postoperative recurrence risk of the target patient.

[0066] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0067] a memory storing at least one instruction; and

[0068] The processor executes the instructions stored in the memory to implement the above-mentioned method for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis.

[0069] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for assessing the risk of postoperative recurrence of liver cancer based on metabolomics analysis.

[0070] The present invention is to solve the problems described in the background technology. The present invention obtains a urine analyzer, an automatic biochemical analyzer and a CT scanner. It can be seen that the embodiment of the present invention obtains the urine analyzer, the automatic biochemical analyzer and the CT scanner in advance, which is convenient for subsequent acquisition of the relevant metabolite levels and liver images of the target patient, thereby comprehensively analyzing and evaluating the patient's liver cancer recurrence risk, improving the accuracy and automation of evaluating the postoperative recurrence risk, and then confirming the target patient, obtaining the patient ID of the target patient, and reading the patient portrait information in the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking Age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data include: dosage and medication cycle. It can be seen that the embodiment of the present invention reads out the patient portrait information, which is convenient for further evaluating the comprehensive health behavior index and metabolite risk value according to the patient's age, body mass index and lifestyle in the patient portrait information, pre-processes the patient portrait information, obtains the smoking impact index and the comprehensive drug value, and calculates the comprehensive health behavior index based on the smoking impact index and the comprehensive drug value. It can be seen that the embodiment of the present invention quantifies the degree of damage to the target patient's physical health caused by the smoking behavior of the target patient and the degree of damage to the target patient's liver caused by the drugs taken by the target patient by calculating the smoking impact index and the comprehensive drug value, and provides The accuracy of assessing the postoperative recurrence risk of liver cancer patients is improved, and the historical test data of the target patient is obtained, wherein the historical test data include: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration and initial choline concentration. The metabolite risk value is obtained through the historical test data, urine analyzer and automatic biochemical analyzer. It can be seen that the embodiment of the present invention obtains the historical test data of the target patient in advance, and uses the urine analyzer and automatic biochemical analyzer to test the target patient, and compares the test results with the historical test data to calculate the metabolite risk value, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. A CT scanner is used to obtain a postoperative liver image of the target patient, and the postoperative liver image is used to calculate the metabolite risk value. Calculate the severity of the lesion. It can be seen that the embodiment of the present invention obtains the difference by comparing the liver images of the patient before and after surgery, the liver images of the patient after surgery and the liver images of healthy people, and comprehensively calculates the severity of the lesion based on the difference, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient. It can be seen that the embodiment of the present invention calculates the recurrence risk value by combining the comprehensive health behavior index, metabolite risk value and lesion severity, comprehensively considering the impact of the comprehensive health behavior index, metabolite risk value and lesion severity on the patient's recurrence risk, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. Therefore, the present invention can improve the accuracy and automation of assessing the postoperative recurrence risk of liver cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the process of a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to one embodiment of the present invention;

[0072] Figure 2 This is a functional module diagram of a liver cancer recurrence risk assessment system based on metabolomics analysis provided by one embodiment of the present invention;

[0073] Figure 3 A schematic structural diagram of an electronic device for implementing the method and system for assessing the risk of liver cancer recurrence after surgery based on metabolomics analysis, provided in one embodiment of the present invention.

[0074] Description of reference numerals:

[0075] 1. Electronic device; 10. Processor; 11. Storage; 12. Bus.

[0076] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0077] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0078] The embodiment of the present application provides a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis. The execution subject of the method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0079] Reference Figure 1 FIG2 is a flow chart of a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to an embodiment of the present invention. In this embodiment, the method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis include:

[0080] S1. Obtain a urine analyzer, an automatic biochemical analyzer, and a CT scanner.

[0081] It should be explained that the urine analyzer is an instrument that can measure the urobilin concentration and urine protein concentration in the urine of the target patient. Optionally, the Sysmex UC-3500 fully automatic urine analyzer is used as the urine analyzer. The automatic biochemical analyzer is an instrument that can measure the alpha-embryonic protein concentration in the blood and the choline concentration in the plasma of the target patient. Optionally, Roche Cedex Bio is used as the automatic biochemical analyzer. The CT scanner is an X-ray scanner that can perform tomographic scanning on the target patient and obtain postoperative liver images. Optionally, Philips core 128 is used as the CT scanner.

[0082] S2. Identify the target patient, obtain the patient ID of the target patient, and read the patient portrait information from the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data includes: dosage and medication cycle.

[0083] It should be understood that the target patient refers to a liver cancer patient who has undergone surgery. In an embodiment of the present invention, before the hospital conducts a liver cancer recurrence risk assessment on the target patient after surgery, it will generate a number for the target patient in the hospital database to identify the target patient. The number is the patient ID, and the patient IDs of different target patients are different. Each patient ID corresponds to a target patient. The hospital database is a database for storing patient portrait information of all patients. When it is necessary to query the patient portrait information of a target patient, the patient portrait information corresponding to the target patient can be retrieved from the hospital database through the patient ID.

[0084] It should be explained that patient age refers to the target patient's age, body mass index refers to the target patient's body mass index, and drinking frequency, exercise frequency, and staying up late frequency respectively refer to the number of times the target patient drank more than 100 ml of alcohol in a single session in the past week, the number of times the target patient ran more than 500 meters in a single session in the past week, and the number of times the target patient fell asleep after midnight in the past week. The average number of cigarettes smoked per day refers to the average number of cigarettes smoked by the target patient per day in the past week. The smoking age refers to the time elapsed from the start of smoking to the present, and the time is measured in years. For example, if the target patient started smoking at the age of 18 and is currently 58 years old, the target patient's smoking age is 40 years. . Medication data consists of dosage and duration of use, and the dosage refers to the amount of a certain drug taken by the target patient in a single session in the past year. The duration of use refers to the duration of the target patient's use of a certain drug in the past year. For example, if the target patient took 0.5 mg of (entecavir) each time in the past year and the drug lasted for one month, the dosage would be 0.5 mg and the duration of use would be 30 days. Furthermore, the drinking frequency, average number of cigarettes smoked per day, smoking age, exercise frequency, frequency of staying up late, and multiple medication data are all registered in advance by the target patient and stored in the hospital database.

[0085] S3. Obtain historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration.

[0086] It should be noted that historical test data were obtained from the hospital during pre-operative physical examinations of target patients and stored in the hospital database. Initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration refer to the target patient's pre-operative urine urobilinogen concentration, urine protein concentration, blood alpha-fetoprotein concentration, and blood choline concentration, respectively.

[0087] S4. Preprocess the patient portrait information to obtain the smoking impact index and the comprehensive drug value, and calculate the comprehensive health behavior index based on the smoking impact index and the comprehensive drug value.

[0088] Specifically, the patient portrait information is pre-processed to obtain the smoking impact index and the comprehensive drug value, including:

[0089] The smoking impact index is calculated based on the average number of cigarettes smoked per day and the length of smoking history in the patient profile information. The calculation formula is as follows:

[0090]

[0091] Wherein, A represents the smoking impact index, B represents the average number of cigarettes smoked per day, N represents the smoking age, α1 and α2 are the preset first impact coefficient and the preset second impact coefficient respectively, and ln is the natural logarithm;

[0092] Confirm the age correction value based on the patient's age in the patient portrait information;

[0093] The comprehensive drug value is calculated based on multiple medication data and age correction values ​​in the patient portrait information. The calculation formula is as follows:

[0094]

[0095] Among them, M represents the comprehensive value of the drug, n represents the number of drug data in multiple drug data, J i represents the dosage of the i-th medication data in multiple medication data, T i A represents the taking period of the i-th medication data in multiple medication data. adj Indicates the age correction value.

[0096] It should be explained that the smoking impact index is used to assess the degree of damage caused to the target patient's physical health by their smoking behavior. The larger the smoking impact index, the greater the degree of damage caused to the target patient's physical health by their smoking behavior. The first impact coefficient and the second impact coefficient are both values ​​set manually by the hospital's doctors. Optionally, the values ​​of the first impact coefficient and the second impact coefficient are 5 and 0.3, respectively. The comprehensive drug value reflects the degree of damage to the target patient's liver caused by the drugs taken by the target patient. The larger the comprehensive drug value, the greater the degree of damage to the target patient's liver caused by the drugs taken by the target patient.

[0097] Specifically, determining the age correction value based on the patient's age in the patient portrait information includes:

[0098] The age correction value is calculated using the following formula:

[0099]

[0100] Where age represents the patient's age.

[0101] Specifically, the comprehensive health behavior index is calculated based on the smoking impact index and the comprehensive drug value, including:

[0102] The comprehensive health behavior index is calculated based on the smoking impact index, drinking frequency, staying up late frequency, exercise frequency, and drug comprehensive value. The calculation formula is as follows:

[0103]

[0104] Among them, Z represents the comprehensive health behavior index, C represents the frequency of drinking, A represents the smoking impact index, S represents the frequency of exercise, Y represents the frequency of staying up late, M represents the comprehensive value of drugs, tanh represents the hyperbolic tangent function, and log represents the logarithmic function.

[0105] It should be explained that the comprehensive health behavior index reflects the health level of the target patient. The larger the comprehensive health behavior index is, the healthier the target patient is.

[0106] S5. Obtain metabolite risk values ​​based on historical test data, urine analyzer and automatic biochemical analyzer.

[0107] In detail, the metabolite risk value is obtained based on historical test data, urine analyzer and automatic biochemical analyzer, including:

[0108] Collecting a first sample from a target patient at a preset first collection time, wherein the first sample includes: first venous blood and first urine;

[0109] Using a urine analyzer to test the first urine to obtain a first urobilinogen concentration and a first urine protein concentration;

[0110] Separating the first venous blood to obtain a first serum and a first plasma;

[0111] The first serum and the first plasma are analyzed respectively by an automatic biochemical analyzer to obtain a first alpha-embryon concentration and a first choline concentration;

[0112] Calculating a urobilin fluctuation value based on the first urobilin concentration and the initial urobilin concentration in the historical test data;

[0113] Comparing urobilinogen fluctuation values ​​with pre-set fluctuation thresholds;

[0114] If the urobilin fluctuation value is less than or equal to the fluctuation threshold, the first urobilin concentration is used as the first target urobilin concentration;

[0115] If the urobilinogen fluctuation value is greater than the fluctuation threshold, a first target urobilinogen concentration is calculated based on the first urobilinogen concentration and the initial urobilinogen concentration, wherein the first target urobilinogen concentration is an average of the first urobilinogen concentration and the initial urobilinogen concentration;

[0116] Obtaining a first target urine protein concentration based on the fluctuation threshold, the first urine protein concentration, and the initial urine protein concentration; obtaining a first target alpha-fetoprotein concentration based on the fluctuation threshold, the first alpha-fetoprotein concentration, and the initial alpha-fetoprotein concentration; and obtaining a first target choline concentration based on the fluctuation threshold, the first choline concentration, and the initial choline concentration;

[0117] Calculating a first metabolite risk value according to a first target urobilinogen concentration, a first target urinary protein concentration, a first target alpha-embryonin concentration, and a first target choline concentration;

[0118] Obtaining a second metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset second collection time; obtaining a third metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset third collection time;

[0119] A metabolite risk value is calculated according to the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value, wherein the metabolite risk value is an average of the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value.

[0120] It should be explained that the first collection time, the second collection time and the third collection time are all times set by the doctor according to the physical condition of the target patient. Optionally, the first collection time is 10:00, the second collection time is 16:00, and the third collection time is 19:00.

[0121] It can be understood that the embodiment of the present invention reduces the error caused by fluctuations in the physical condition of the target patient by collecting venous blood and urine from the target patient at multiple time points and subsequently calculating the average value of the first metabolite risk value, the second metabolite risk value and the third metabolite risk value.

[0122] It should be explained that the first sample includes: first venous blood and first urine. The first venous blood refers to the venous blood of the target patient collected at the first collection time, and the first urine refers to the urine of the target patient collected at the first collection time. The first urobilin concentration and the first urine protein concentration are the concentrations of urobilin and urine protein in the first urine, respectively. The first serum refers to the serum separated from the first venous blood, and the first plasma refers to the plasma separated from the first venous blood. The first alpha-fetoprotein concentration refers to the concentration of alpha-fetoprotein in the first serum, and the first choline concentration refers to the concentration of choline in the first plasma. Optionally, the fluctuation threshold is 10%.

[0123] It is understood that the technology of using a urine analyzer to test the first urine to obtain the first urobilin concentration and the first urine protein concentration is prior art and will not be described in detail here. The technology of using an automatic biochemical analyzer to analyze the first serum and the first plasma to obtain the first alpha-embryon protein concentration and the first choline concentration is prior art and will not be described in detail here.

[0124] It should be understood that the method for obtaining the first target urine protein concentration based on the fluctuation threshold, the first urine protein concentration, and the initial urine protein concentration, the method for obtaining the first target alpha-fetoprotein concentration based on the fluctuation threshold, the first alpha-fetoprotein concentration, and the initial alpha-fetoprotein concentration, and the method for obtaining the first target choline concentration based on the fluctuation threshold, the first choline concentration, and the initial choline concentration are all the same as the method for obtaining the first target urobilin concentration using the fluctuation threshold, the first urobilin concentration, and the initial urobilin concentration, and are not described in detail here.

[0125] It is understood that the method for obtaining a second metabolite risk value based on a urine analyzer, an automatic biochemical analyzer, a fluctuation threshold, a target patient, and a preset second collection time, and the method for obtaining a third metabolite risk value based on a urine analyzer, an automatic biochemical analyzer, a fluctuation threshold, a target patient, and a preset third collection time are the same as the method for obtaining a first metabolite risk value using a urine analyzer, an automatic biochemical analyzer, a fluctuation threshold, a target patient, and a preset first collection time, and are not further described here. The metabolite risk value reflects the metabolic capacity of the target patient's liver; a higher metabolite risk value indicates a lower metabolic capacity of the target patient's liver.

[0126] In detail, the calculation formula of the urobilinogen fluctuation value is as follows:

[0127]

[0128] Among them, Δ URO is the fluctuation value of urobilinogen, [URO x ] and [URO0] are the first urobilinogen concentration and the initial urobilinogen concentration, respectively, and || refers to the absolute value.

[0129] In detail, the calculation formula of the risk value of the first metabolite is as follows:

[0130] R d =β1×e ([URO]×[PRO]) +β2×ln(1+[AFP])(1+[Chol])

[0131] Among them, R d represents the first metabolite risk value, [URO] represents the first target urobilinogen concentration, [PRO] represents the first target urinary protein concentration, [AFP] represents the first target alpha-fetoprotein concentration, [Chol] represents the first target choline concentration, β1 and β2 are the preset first coefficient and the preset second coefficient, respectively, and e is a natural constant.

[0132] It should be explained that the urobilin fluctuation value reflects the degree of difference between the target patient's first urobilin concentration and the initial urobilin concentration. The larger the urobilin fluctuation value, the greater the degree of difference between the target patient's first urobilin concentration and the initial urobilin concentration. The first metabolite risk value reflects the target patient's liver metabolic capacity at the first collection time. The larger the first metabolite risk value, the worse the target patient's liver metabolic capacity at the first collection time. The first coefficient and the second coefficient are both values ​​set by the hospital's doctors. Optionally, the first coefficient and the second coefficient can be 2 and 3, respectively.

[0133] S6. Obtain a postoperative liver image of the target patient using a CT scanner, and calculate the severity of the lesion using the postoperative liver image.

[0134] In detail, the method of obtaining a postoperative liver image of a target patient using a CT scanner includes:

[0135] Confirm the fixed patient based on the CT scanner and the target patient;

[0136] Confirm radiation dose and tube voltage based on body mass index;

[0137] The radiation dose and tube voltage are input into the CT scanner to obtain a target CT scanner;

[0138] The target CT scanner is started, and a postoperative liver image of the fixed patient is acquired using the started target CT scanner.

[0139] It is understandable that the confirmation of the fixed patient based on the CT scanner and the target patient means that when it is confirmed that the target patient is placed on the CT scanner and can be scanned by the CT scanner, the target patient at this time is used as the fixed patient.

[0140] Specifically, determining the radiation dose and tube voltage based on the body mass index includes:

[0141] The tube voltage is calculated according to the body mass index. The calculation formula is as follows:

[0142]

[0143] Among them, U fs represents tube voltage, and BMI represents body mass index.

[0144] The radiation dose is calculated based on the body mass index using the following formula:

[0145]

[0146] Among them, H fs Indicates radiation dose.

[0147] It should be explained that the use of the activated target CT scanner to obtain a postoperative liver image of a fixed patient refers to: using the activated target CT scanner to scan a preset first position on the fixed patient, and using the image output after the target CT scanner scans the preset first position on the fixed patient as the postoperative liver image. The first position refers to: the area on the target patient's body where the liver is located. The postoperative liver image refers to a liver CT image taken of the target patient after the liver cancer surgery is completed, and the fixed patient refers to the target patient who is placed on the CT scanner and is about to be scanned. In this embodiment of the present invention, scanning refers to: electronic computed tomography scanning.

[0148] It's understood that radiation dose refers to the amount of X-rays emitted per unit time by the X-ray tube in a CT scanner, typically expressed in milliampere-seconds (mAs), reflecting the intensity of the X-rays. Tube voltage refers to the voltage across the X-ray tube in a CT scanner, typically expressed in kilovolts (kV), reflecting the penetrating power of the X-rays. Higher tube voltages increase the penetrating power of the X-rays.

[0149] It should be explained that the target CT scanner refers to a CT scanner to which the radiation dose and tube voltage are input.

[0150] Specifically, the calculation of lesion severity using postoperative liver images includes:

[0151] Obtain preoperative liver images of target patients and liver images of healthy subjects;

[0152] Inputting the preoperative liver image and the postoperative liver image into a pre-built image processing model to obtain a first analysis model, and obtaining a first difference degree using the first analysis model;

[0153] Obtaining a second difference based on liver images of healthy people, liver images after surgery, and an image processing model;

[0154] The severity of the lesion is calculated using the first difference and the second difference. The calculation formula is as follows:

[0155]

[0156] Where χ represents the severity of the lesion, ε1 represents the first difference, and ε2 represents the second difference.

[0157] It should be explained that the severity of the lesion reflects the degree of liver lesions in the target patient after surgery. The higher the severity of the lesion, the higher the degree of liver lesions in the target patient after surgery. The preoperative liver image refers to the image obtained by scanning the first position on the target patient using a CT scanner before the completion of the liver cancer surgery. The liver image of a healthy person refers to the image obtained by the hospital using a CT scanner to scan the area where the liver is located on the body of a healthy person in advance. The main operating principle of the image processing model is: first, the convolutional neural network in the image processing model is used to perform multi-layer convolution, pooling and activation function processing on the preoperative liver image and the postoperative liver image to obtain two eigenvectors, and then the cosine similarity of the two eigenvectors is calculated, and then the absolute difference between the cosine similarity and 1 is calculated. The absolute difference is the first difference, and the above process is a disclosed technical solution, and the embodiment of the present invention will not be repeated here.

[0158] It should be understood that the method for obtaining the second difference based on liver images of healthy people, postoperative liver images and image processing models is the same as the method for obtaining the first difference using preoperative liver images, postoperative liver images and image processing models, and will not be repeated here.

[0159] S7. Calculate the recurrence risk value based on the comprehensive health behavior index, metabolite risk value, and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient.

[0160] Specifically, the recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value, and lesion severity, including:

[0161] The recurrence risk value was calculated using the comprehensive health behavior index, metabolite risk value, and lesion severity. The calculation formula is as follows:

[0162]

[0163] Among them, RS represents the recurrence risk value, Z0 is the preset mean of health behavior index, R dx represents the metabolite risk value, γ1, γ2 and γ3 are the preset first risk coefficient, the preset second risk coefficient and the preset third risk coefficient respectively.

[0164] It should be understood that the embodiment of the present invention calculates the recurrence risk value by combining the comprehensive health behavior index, metabolite risk value, and lesion severity. Since the comprehensive health behavior index reflects the health status of the target patient in terms of smoking, drinking, staying up late, exercising, and taking medication, the metabolite risk value reflects the metabolic capacity of the target patient's liver, and the lesion severity reflects the degree of liver lesions after surgery in the target patient, the recurrence risk value reflects the possibility of tumor cells reappearing and growing in the target patient after liver cancer surgery. The greater the recurrence risk value, the greater the possibility of tumor cells reappearing and growing in the target patient after liver cancer surgery. The mean value of the health behavior index, the first risk coefficient, the second risk coefficient, and the third risk coefficient are all values ​​manually set by doctors in the hospital. Optionally, the values ​​of the mean value of the health behavior index, the first risk coefficient, the second risk coefficient, and the third risk coefficient are 50, 2, 3, and 5, respectively.

[0165] For example, after calculating the recurrence risk value, the doctor can evaluate the risk of postoperative recurrence of the target patient based on the recurrence risk value. For example, the greater the recurrence risk value, the greater the risk of postoperative recurrence of the target patient.

[0166] The present invention is to solve the problems described in the background technology. The present invention obtains a urine analyzer, an automatic biochemical analyzer and a CT scanner. It can be seen that the embodiment of the present invention obtains the urine analyzer, the automatic biochemical analyzer and the CT scanner in advance, which is convenient for subsequent acquisition of the relevant metabolite levels and liver images of the target patient, thereby comprehensively analyzing and evaluating the patient's liver cancer recurrence risk, improving the accuracy and automation of evaluating the postoperative recurrence risk, and then confirming the target patient, obtaining the patient ID of the target patient, and reading the patient portrait information in the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking Age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data include: dosage and medication cycle. It can be seen that the embodiment of the present invention reads out the patient portrait information, which is convenient for further evaluating the comprehensive health behavior index and metabolite risk value according to the patient's age, body mass index and lifestyle in the patient portrait information, pre-processes the patient portrait information, obtains the smoking impact index and the comprehensive drug value, and calculates the comprehensive health behavior index based on the smoking impact index and the comprehensive drug value. It can be seen that the embodiment of the present invention quantifies the degree of damage to the target patient's physical health caused by the smoking behavior of the target patient and the degree of damage to the target patient's liver caused by the drugs taken by the target patient by calculating the smoking impact index and the comprehensive drug value, and provides The accuracy of assessing the postoperative recurrence risk of liver cancer patients is improved, and the historical test data of the target patient is obtained, wherein the historical test data include: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration and initial choline concentration. The metabolite risk value is obtained through the historical test data, urine analyzer and automatic biochemical analyzer. It can be seen that the embodiment of the present invention obtains the historical test data of the target patient in advance, and uses the urine analyzer and automatic biochemical analyzer to test the target patient, and compares the test results with the historical test data to calculate the metabolite risk value, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. A CT scanner is used to obtain a postoperative liver image of the target patient, and the postoperative liver image is used to calculate the metabolite risk value. Calculate the severity of the lesion. It can be seen that the embodiment of the present invention obtains the difference by comparing the liver images of the patient before and after surgery, the liver images of the patient after surgery and the liver images of healthy people, and comprehensively calculates the severity of the lesion based on the difference, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient. It can be seen that the embodiment of the present invention calculates the recurrence risk value by combining the comprehensive health behavior index, metabolite risk value and lesion severity, comprehensively considering the impact of the comprehensive health behavior index, metabolite risk value and lesion severity on the patient's recurrence risk, thereby improving the accuracy of assessing the postoperative recurrence risk of liver cancer patients. Therefore, the present invention can improve the accuracy and automation of assessing the postoperative recurrence risk of liver cancer patients.

[0167] like Figure 2 , which is a functional module diagram of a liver cancer postoperative recurrence risk assessment system based on metabolomics analysis provided by one embodiment of the present invention.

[0168] The metabolomics-based liver cancer recurrence risk assessment system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the metabolomics-based liver cancer recurrence risk assessment system 100 can include an initial instrument preparation module 101, a health value confirmation module 102, a patient information processing module 103, and a patient risk assessment module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0169] The initial instrument preparation module 101 is used to obtain a urine analyzer, an automatic biochemical analyzer, and a CT scanner;

[0170] The health value confirmation module 102 is used to confirm the target patient, obtain the patient ID of the target patient, and read the patient portrait information from the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data includes: dosage and medication cycle, and obtain historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration and initial choline concentration;

[0171] The patient information processing module 103 is configured to pre-process the patient portrait information to obtain a smoking impact index and a comprehensive medication value, calculate a comprehensive health behavior index based on the smoking impact index and the comprehensive medication value, obtain a metabolite risk value based on historical test data, a urine analyzer, and an automatic biochemical analyzer, obtain a postoperative liver image of the target patient using a CT scanner, and calculate the severity of the lesion using the postoperative liver image;

[0172] The patient risk assessment module 104 is used to calculate the recurrence risk value based on the comprehensive health behavior index, metabolite risk value and lesion severity, and complete the assessment of the postoperative recurrence risk of the target patient.

[0173] In detail, the modules in the liver cancer recurrence risk assessment system 100 based on metabolomics analysis in the embodiment of the present invention are used in the same manner as above. Figure 1The method and system for assessing the risk of recurrence after liver cancer surgery based on metabolomics analysis described in

[15] are the same technical means and can produce the same technical effects, so they will not be repeated here.

[0174] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing a method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis, provided by one embodiment of the present invention.

[0175] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method and system program for assessing the risk of postoperative recurrence of liver cancer based on metabolomics analysis.

[0176] The memory 11 includes at least one type of readable storage medium, including a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the method and system program for assessing the risk of recurrence after liver cancer surgery based on metabolomics analysis, but can also be used to temporarily store data that has been output or is to be output.

[0177] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (such as a method and system program for assessing the risk of postoperative recurrence of liver cancer based on metabolomics analysis), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0178] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0179] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0180] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0181] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0182] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0183] The method and system program for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When executed in the processor 10, the following can be achieved:

[0184] Obtain urine analyzers, automated biochemical analyzers, and CT scanners;

[0185] Identify the target patient, obtain the patient ID of the target patient, and read the patient profile information from the pre-built hospital database based on the patient ID. The patient profile information includes: patient age, body mass index, and lifestyle. The lifestyle information includes: drinking frequency, average number of cigarettes smoked per day, smoking duration, exercise frequency, frequency of staying up late, and multiple medication data. The medication data includes: dosage and duration of use.

[0186] Obtaining historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration;

[0187] Preprocess the patient portrait information to obtain the smoking impact index and drug comprehensive value, and calculate the comprehensive health behavior index based on the smoking impact index and drug comprehensive value;

[0188] Obtain metabolite risk values ​​based on historical test data, urine analyzers, and automated biochemical analyzers;

[0189] Using a CT scanner to obtain postoperative liver images of the target patient, and using the postoperative liver images to calculate the severity of the lesion;

[0190] The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient.

[0191] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0192] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0193] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0194] Obtain urine analyzers, automated biochemical analyzers, and CT scanners;

[0195] Identify the target patient, obtain the patient ID of the target patient, and read the patient profile information from the pre-built hospital database based on the patient ID. The patient profile information includes: patient age, body mass index, and lifestyle. The lifestyle information includes: drinking frequency, average number of cigarettes smoked per day, smoking duration, exercise frequency, frequency of staying up late, and multiple medication data. The medication data includes: dosage and duration of use.

[0196] Obtaining historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration;

[0197] Preprocess the patient portrait information to obtain the smoking impact index and drug comprehensive value, and calculate the comprehensive health behavior index based on the smoking impact index and drug comprehensive value;

[0198] Obtain metabolite risk values ​​based on historical test data, urine analyzers, and automated biochemical analyzers;

[0199] Using a CT scanner to obtain postoperative liver images of the target patient, and using the postoperative liver images to calculate the severity of the lesion;

[0200] The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient.

[0201] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0202] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0203] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0204] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis, characterized in that: The method comprises: Obtain urine analyzers, automated biochemical analyzers, and CT scanners; Identify the target patient, obtain the patient ID of the target patient, and read the patient profile information from the pre-built hospital database based on the patient ID. The patient profile information includes: patient age, body mass index, and lifestyle. The lifestyle information includes: drinking frequency, average number of cigarettes smoked per day, smoking duration, exercise frequency, frequency of staying up late, and multiple medication data. The medication data includes: dosage and duration of use. Obtaining historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration, and initial choline concentration; Preprocess the patient portrait information to obtain the smoking impact index and drug comprehensive value, and calculate the comprehensive health behavior index based on the smoking impact index and drug comprehensive value; Obtain metabolite risk values ​​based on historical test data, urine analyzers, and automated biochemical analyzers; Using a CT scanner to obtain postoperative liver images of the target patient, and using the postoperative liver images to calculate the severity of the lesion; The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity to complete the assessment of the postoperative recurrence risk of the target patient.

2. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 1, characterized in that: The patient portrait information is pre-processed to obtain the smoking impact index and the comprehensive drug value, including: The smoking impact index is calculated based on the average number of cigarettes smoked per day and the length of smoking history in the patient profile information. The calculation formula is as follows: Wherein, A represents the smoking impact index, B represents the average number of cigarettes smoked per day, N represents the smoking age, α1 and α2 are the preset first impact coefficient and the preset second impact coefficient respectively, and ln is the natural logarithm; Confirm the age correction value based on the patient's age in the patient portrait information; The comprehensive drug value is calculated based on multiple medication data and age correction values ​​in the patient portrait information. The calculation formula is as follows: Among them, M represents the comprehensive value of the drug, n represents the number of drug data in multiple drug data, J i represents the dosage of the i-th medication data in multiple medication data, T i A represents the taking period of the i-th medication data in multiple medication data. adj Indicates the age correction value.

3. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 2, wherein: The comprehensive health behavior index is calculated based on the smoking impact index and the comprehensive drug value, including: The comprehensive health behavior index is calculated based on the smoking impact index, drinking frequency, staying up late frequency, exercise frequency, and drug comprehensive value. The calculation formula is as follows: Among them, Z represents the comprehensive health behavior index, C represents the frequency of drinking, A represents the smoking impact index, S represents the frequency of exercise, Y represents the frequency of staying up late, M represents the comprehensive value of drugs, tanh represents the hyperbolic tangent function, and log represents the logarithmic function.

4. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 3, characterized in that: The metabolite risk value is obtained based on historical test data, urine analyzer and automatic biochemical analyzer, including: Collecting a first sample from a target patient at a preset first collection time, wherein the first sample includes: first venous blood and first urine; Using a urine analyzer to test the first urine to obtain a first urobilinogen concentration and a first urine protein concentration; Separating the first venous blood to obtain a first serum and a first plasma; The first serum and the first plasma are analyzed respectively by an automatic biochemical analyzer to obtain a first alpha-embryon concentration and a first choline concentration; Calculating a urobilin fluctuation value based on the first urobilin concentration and the initial urobilin concentration in the historical test data; Comparing urobilinogen fluctuation values ​​with pre-set fluctuation thresholds; If the urobilin fluctuation value is less than or equal to the fluctuation threshold, the first urobilin concentration is used as the first target urobilin concentration; If the urobilinogen fluctuation value is greater than the fluctuation threshold, a first target urobilinogen concentration is calculated based on the first urobilinogen concentration and the initial urobilinogen concentration, wherein the first target urobilinogen concentration is an average of the first urobilinogen concentration and the initial urobilinogen concentration; Obtaining a first target urine protein concentration based on the fluctuation threshold, the first urine protein concentration, and the initial urine protein concentration; obtaining a first target alpha-fetoprotein concentration based on the fluctuation threshold, the first alpha-fetoprotein concentration, and the initial alpha-fetoprotein concentration; and obtaining a first target choline concentration based on the fluctuation threshold, the first choline concentration, and the initial choline concentration; Calculating a first metabolite risk value according to a first target urobilinogen concentration, a first target urinary protein concentration, a first target alpha-embryonin concentration, and a first target choline concentration; Obtaining a second metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset second collection time; obtaining a third metabolite risk value based on the urine analyzer, the automatic biochemical analyzer, the fluctuation threshold, the target patient, and the preset third collection time; A metabolite risk value is calculated according to the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value, wherein the metabolite risk value is an average of the first metabolite risk value, the second metabolite risk value, and the third metabolite risk value.

5. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 4, characterized in that: The calculation formula of the urobilinogen fluctuation value is as follows: Among them, Δ URO is the fluctuation value of urobilinogen, [URO x ] and [URO0] are the first urobilinogen concentration and the initial urobilinogen concentration, respectively, and | | refers to the absolute value.

6. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 5, characterized in that: The calculation formula of the risk value of the first metabolite is as follows: R d =β1×e ([URO]×[PRO]) +β2×ln(1+[AFP])(1+[Chol]) Among them, R d represents the first metabolite risk value, [URO] represents the first target urobilinogen concentration, [PRO] represents the first target urinary protein concentration, [AFP] represents the first target alpha-fetoprotein concentration, [Chol] represents the first target choline concentration, β1 and β2 are the preset first coefficient and the preset second coefficient, respectively, and e is a natural constant.

7. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 6, characterized in that: The method of obtaining a postoperative liver image of a target patient by using a CT scanner includes: Confirm the fixed patient based on the CT scanner and the target patient; Confirm radiation dose and tube voltage based on body mass index; The radiation dose and tube voltage are input into the CT scanner to obtain a target CT scanner; The target CT scanner is started, and a postoperative liver image of the fixed patient is acquired using the started target CT scanner.

8. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 7, characterized in that: The calculation of lesion severity using postoperative liver images includes: Obtain preoperative liver images of target patients and liver images of healthy subjects; Inputting the preoperative liver image and the postoperative liver image into a pre-built image processing model to obtain a first analysis model, and obtaining a first difference degree using the first analysis model; Obtaining a second difference based on liver images of healthy people, liver images after surgery, and an image processing model; The severity of the lesion is calculated using the first difference and the second difference. The calculation formula is as follows: Where χ represents the severity of the lesion, ε1 represents the first difference, and ε2 represents the second difference.

9. The method and system for assessing the risk of recurrence of liver cancer after surgery based on metabolomics analysis according to claim 8, characterized in that: The recurrence risk value is calculated based on the comprehensive health behavior index, metabolite risk value and lesion severity, including: The recurrence risk value was calculated using the comprehensive health behavior index, metabolite risk value, and lesion severity. The calculation formula is as follows: Among them, RS represents the recurrence risk value, Z0 is the preset mean of health behavior index, R dx represents the metabolite risk value, γ1, γ2 and γ3 are the preset first risk coefficient, the preset second risk coefficient and the preset third risk coefficient respectively.

10. A liver cancer recurrence risk assessment system based on metabolomics analysis, characterized in that: The system comprises: an initial instrument preparation module for acquiring a urine analyzer, an automated biochemistry analyzer, and a CT scanner; The health value confirmation module is used to confirm the target patient, obtain the patient ID of the target patient, and read the patient portrait information from the pre-built hospital database based on the patient ID, wherein the patient portrait information includes: patient age, body mass index and lifestyle, wherein the lifestyle includes: drinking frequency, average number of cigarettes smoked per day, smoking age, exercise frequency, frequency of staying up late and multiple medication data, wherein the medication data includes: dosage and medication cycle, and obtain the historical test data of the target patient, wherein the historical test data includes: initial urobilinogen concentration, initial urine protein concentration, initial alpha-fetoprotein concentration and initial choline concentration; The patient information processing module is used to pre-process patient portrait information to obtain a smoking impact index and a comprehensive medication value. Based on the smoking impact index and the comprehensive medication value, a comprehensive health behavior index is calculated. Metabolite risk values ​​are obtained based on historical test data, a urine analyzer, and an automatic biochemical analyzer. Postoperative liver images of target patients are obtained using a CT scanner and the lesion severity is calculated using the postoperative liver images. The patient risk assessment module is used to calculate the recurrence risk value based on the comprehensive health behavior index, metabolite risk value and lesion severity, and complete the assessment of the postoperative recurrence risk of the target patient.

Images