Liver and gall tumor follow-up visit data processing service system

Abstract

The invention discloses a hepatobiliary tumor follow-up visit data processing service system, and relates to the technical field of hepatobiliary tumor follow-up visit data processing. A data collection module is used for collecting hepatobiliary tumor growth and change conditions of a current patient in a current follow-up visit period; the tumor volume change rate OBL, the tumor growth speed OTS and the tumor growth predicted value OY are sequentially calculated and output, the change trend of the liver and gall tumors of the current patient is observed, basic data are provided for follow-up visit diagnosis and treatment, and the current patient is in the next follow-up visit period through a data prediction processing module. And taking the tumor growth predicted value OY of the last follow-up period as an input value of the tumor volume Ostart when follow-up of the next follow-up period starts to form continuous calculation of follow-up, so that efficient and accurate processing and analysis of tumor data are realized, the prediction accuracy is improved, and errors generated by prediction are corrected and adjusted in time; and more accurate treatment suggestions are provided for patients.

Description

Technical Field

[0001] The present invention relates to the technical field of hepatobiliary tumor follow-up data processing, and in particular to a hepatobiliary tumor follow-up data processing service system. Background Art

[0002] With the continuous advancement of information technology, medical informatization has become an important trend in the development of the medical industry. Medical informatization has not only improved the quality and efficiency of medical services, but also promoted the optimal allocation and efficient utilization of medical resources. Among them, hepatobiliary tumors are one of the common malignant tumors. In the processing of hepatobiliary tumor follow-up data, the application of medical informatization technology has made data collection and storage more convenient and efficient. Tumor follow-up management refers to regular follow-up, monitoring and evaluation of tumor patients to understand their condition changes, treatment effects and prognosis. For patients with hepatobiliary tumors, follow-up management is particularly important. Through follow-up, doctors can promptly discover changes in patients' conditions, adjust treatment plans, and improve treatment effects. At the same time, follow-up data can also provide valuable information for scientific research and promote the advancement of tumor prevention and treatment technology.

[0003] At present, the existing follow-up management systems often simply store data, lack intelligent analysis and prediction functions, and cannot provide doctors with valuable reference information. In addition, due to the complexity and uncertainty of tumor growth, existing prediction methods often find it difficult to accurately predict the growth trend of tumors, especially in the loop feedback mechanism. Although most data-based prediction methods have a certain degree of accuracy, the variability of tumors cannot be ignored. Therefore, there is an error between the actual tumor growth trend and the prediction. If this error is not corrected, it will seriously affect the accuracy of the prediction. In addition, the tumor growth of each patient is unique, but the existing follow-up data processing system often adopts a unified processing and analysis method and lacks personalized processing strategies. Summary of the invention

[0004] The purpose of the present invention is to provide a hepatobiliary tumor follow-up data processing service system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, and the specific implementation steps are as follows: Step 1: Use the data collection module to collect the growth and changes of hepatobiliary tumors in the current follow-up period of the current patient; Step 2: Using the data prediction processing module, the tumor volume change rate OBL, the tumor growth rate OTS, and the tumor growth prediction value OY are calculated and output in sequence. Based on the tumor growth prediction value OY, the change trend of the current patient's hepatobiliary tumor is observed, and basic data is provided for follow-up diagnosis and treatment; The data prediction and processing module includes a unit for evaluating tumor changes during follow-up, a unit for comprehensively reflecting tumor growth characteristics, a unit for predicting future tumor growth trends, and a unit for error analysis and correction; Step 3: In the next follow-up period, the current patient uses the data prediction processing module and uses the tumor growth prediction value OY of the previous follow-up period as the tumor volume O at the beginning of the next follow-up period. start The input values ​​form the continuous calculation of follow-up; Step 4: Before each follow-up calculation, the error analysis and correction unit is used to compare the tumor growth prediction value OY of the previous follow-up cycle with the tumor volume O at the beginning of the current follow-up cycle. start The errors existing between the two are analyzed and corrected, and acted on the unit for predicting the future growth trend of the tumor to perform adjustment calculations; The equipment used by the data collection module includes a server and a database management system; The equipment used by the data prediction processing module includes data analysis software and a workstation.

[0006] Optionally, the calculation formula for the tumor change unit during the follow-up evaluation is as follows: ; in: OBL is the rate of change of tumor volume; O end is the tumor volume at the end of follow-up, O end Reflects the tumor volume of the current patient at the end of the current follow-up period; O start is the tumor volume at the beginning of follow-up, O start Reflects the tumor volume of the current patient at the beginning of the current follow-up period.

[0007] Optionally, the calculation formula for the unit that fully reflects the tumor growth characteristics is as follows: ; in: OTS is the tumor growth rate; T1 is the follow-up time interval, in days. T1 reflects the time interval of the current patient in the current follow-up cycle, that is, the tumor volume at the beginning of the follow-up measurement. start The tumor volume at the end of follow-up was end The time difference between.

[0008] Optionally, the calculation formula for predicting the future growth trend of the tumor is as follows: ; in: OY is the predicted value of tumor growth; T2 is the predicted time extension, in days. T2 reflects the time interval between the current follow-up period and the next follow-up period of the current patient, that is, the tumor volume at the end of the follow-up measurement in the current follow-up period. end Tumor volume at the beginning of follow-up until the next follow-up period start The time difference between.

[0009] Optionally, based on the tumor growth prediction value OY and the tumor growth prediction value OY of the previous follow-up period, observing the growth change trend of the tumor growth prediction value OY, and treating the patient according to the change trend; If the predicted tumor growth value OY calculated in the current follow-up period = the tumor volume O measured at the beginning of the next follow-up period start , the tumor growth prediction value OY calculated in the current follow-up period is used as the input value, replacing the tumor volume O at the beginning of the follow-up period measured in the next follow-up period start , and input into the unit for evaluating tumor changes during the follow-up process and the unit for comprehensively reflecting tumor growth characteristics for calculation in the next follow-up cycle; If the tumor growth prediction value OY calculated in the current follow-up period is ≠ the tumor volume O measured at the beginning of the next follow-up period start , the error correction coefficient K is calculated by linear regression analysis, and K is entered into the unit for predicting the future growth trend of the tumor in the next follow-up period for adjustment calculation. The specific calculation formula after adjustment is as follows: .

[0010] Optionally, the specific steps of the linear regression analysis are as follows: S1. Set X i is the independent variable, Y i is the dependent variable, and collects the independent variable X i and the dependent variable Y i Observed values ​​and calculate the independent variable X i and the dependent variable Y i The mean of the independent variable X i is the observed value of tumor growth prediction value OY, dependent variable Y i is the observed value of the prediction error C; S2. Use the slope calculation formula and intercept calculation formula to calculate the independent variable X. i and the dependent variable Y i The slope between XL and the intercept J; S3. According to the slope XL and the intercept J, construct the linear equation Y=XL×X+J; S4. In this regression model, the reciprocal of the slope XL is considered as a candidate value of the error correction coefficient K, that is, K=1 / XL.

[0011] Optionally, the calculation formula of the prediction error C is as follows: ; The calculation formula of the slope calculation formula is as follows: ; ; ; n is the total number of observations, and n reflects the total number of observations observed in the previous n cycles for the current patient; X avg is the mean of the independent variable; X1 is the first observed independent variable, X2 is the second observed independent variable, and X3 is the third observed independent variable. n is the nth observed independent variable; Y avg is the mean of the dependent variable; Y1 is the first observed dependent variable, Y2 is the second observed dependent variable, and Y3 is the third observed dependent variable. n is the nth observed dependent variable; The calculation formula of the intercept calculation formula is as follows: .

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention enables the system to quickly obtain the tumor volume change rate OBL, the tumor growth speed OTS and the tumor growth prediction value OY through the automatic calculation of the tumor change situation unit during the evaluation of the follow-up process, the tumor growth characteristic unit, and the tumor future growth trend prediction unit, thereby greatly improving the data processing efficiency. The system uses the tumor change situation unit during the evaluation of the follow-up process, the tumor growth characteristic unit, the tumor future growth trend prediction unit and the error analysis and correction unit to perform intelligent analysis on the tumor data and accurately predict the growth trend of the tumor. At the same time, through the loop feedback mechanism, the system can continuously update the prediction value to realize dynamic monitoring of the tumor growth situation.

[0013] Second, the present invention is aimed at the tumor growth prediction value OY and the tumor volume O at the beginning of the next follow-up cycle start In order to solve the problem of error between the two, the system uses linear regression analysis to calculate the error correction coefficient K, and then corrects the tumor growth prediction value OY, thereby improving the accuracy of the prediction.

[0014] 3. The system of the present invention can provide patients with more accurate treatment recommendations based on the specific conditions of each patient, including tumor type, volume, and growth rate, and adopt personalized treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a method flow chart of the hepatobiliary tumor follow-up data processing service system; Figure 2 It is a flow chart of the method of linear regression analysis in the present invention; Figure 3 It is a structural schematic diagram of the data prediction processing module of the present invention; Figure 4 It is a schematic diagram of the follow-up period and detection interval period of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Regarding this hepatobiliary tumor follow-up data processing service system, it is different from the existing follow-up management system. The existing follow-up management system lacks intelligent analysis and prediction functions, and it is difficult to accurately predict the growth trend of the tumor. There is an error between the actual tumor growth trend and the prediction, and it is impossible to achieve timely and effective correction purposes. It also lacks personalized processing strategies. This algorithm unit realizes efficient and accurate processing and analysis of tumor data, improves the accuracy of the prediction and timely correction and adjustment of prediction errors, and provides patients with more accurate treatment recommendations.

[0018] For example, see Figures 1 to 4 This implementation provides a hepatobiliary tumor follow-up data processing service system, and the specific implementation steps are as follows: Step 1: Use the data collection module to collect the growth and changes of hepatobiliary tumors in the current follow-up period of the current patient; Step 2: Using the data prediction processing module, the tumor volume change rate OBL, the tumor growth rate OTS, and the tumor growth prediction value OY are calculated and output in sequence. Based on the tumor growth prediction value OY, the change trend of the current patient's hepatobiliary tumor is observed, and basic data is provided for follow-up diagnosis and treatment; The data prediction and processing module includes a unit for evaluating tumor changes during follow-up, a unit for comprehensively reflecting tumor growth characteristics, a unit for predicting future tumor growth trends, and a unit for error analysis and correction. Step 3: In the next follow-up period, the current patient uses the data prediction processing module and uses the tumor growth prediction value OY of the previous follow-up period as the tumor volume O at the beginning of the next follow-up period. start The input values ​​form the continuous calculation of follow-up; Step 4: Before each follow-up calculation, the error analysis and correction unit is used to compare the tumor growth prediction value OY of the previous follow-up cycle with the tumor volume O at the beginning of the current follow-up cycle. start The errors between them are analyzed and corrected, and acted on the unit for predicting the future growth trend of the tumor to make adjustment calculations; The equipment used by the data collection module includes servers and database management systems; The equipment used in the data prediction and processing module includes data analysis software and workstations.

[0019] In this embodiment, the system, through the cooperation of three algorithm units, together constitutes an important tool for tumor follow-up and monitoring. Combining the three operation results of OBL, OTS and OY, OBL is the tumor volume change rate. During the tumor follow-up process, doctors need to measure the tumor volume regularly to understand the tumor growth. By comparing the tumor volume O at the beginning of the follow-up, the doctor can obtain the tumor volume OY. start and tumor volume at the end of follow-up end The tumor volume at the time of diagnosis can be used to calculate the tumor volume change rate OBL, which helps doctors judge the effectiveness of the current treatment and serves as a reference for adjusting the treatment plan. As for this value, if the tumor volume change rate OBL is high, it indicates that the tumor is growing rapidly, and treatment needs to be strengthened and the treatment plan needs to be changed. OTS is the tumor growth rate. The tumor growth rate is not only related to the change in tumor volume, but also affected by the follow-up time interval. This indicator helps doctors understand the tumor growth rate more accurately and formulate a more accurate treatment plan. As for this value, more active treatment measures need to be taken for tumors with fast growth rates. OY is the tumor growth prediction rate. The measured value helps doctors understand the future growth trend of the tumor and provides patients with more accurate treatment suggestions. As far as this value is concerned, if the tumor is predicted to grow rapidly in the future, the doctor will recommend early surgery and enhanced drug treatment. The calculation result of OY can also affect the calculation of OBL, OTS and OY in the next follow-up cycle. According to the error in OY prediction, the error correction coefficient K is used to adjust the OY of the next follow-up cycle, so that the three algorithms of this system have a high correlation and entanglement, which helps doctors to more accurately evaluate the growth of the tumor, predict future volume changes, and formulate more reasonable treatment plans and follow-up strategies accordingly.

[0020] See also Figures 1 to 4, the calculation formula for evaluating the tumor change unit during follow-up is as follows: ; in: OBL is the rate of change of tumor volume; O end is the tumor volume at the end of follow-up, O end Reflects the tumor volume of the current patient at the end of the current follow-up period; O start is the tumor volume at the beginning of follow-up, O start Reflects the tumor volume of the current patient at the beginning of the current follow-up period.

[0021] In this embodiment: First, in this algorithm unit This calculation part represents the tumor volume from the tumor volume at the beginning of follow-up. start The tumor volume at the end of follow-up was end The absolute change in the volume of the tumor during this period, that is, the increase or decrease in the volume of the tumor during the current follow-up period, is directly reflected. As the numerator of the calculation formula for evaluating the changes in the tumor during the follow-up process, this difference is used to calculate the relative change percentage of the tumor volume, that is, the tumor volume change rate OBL. The tumor volume change rate OBL is an important indicator for measuring tumor growth and shrinkage, and the overall The calculation part of the tumor volume change rate OBL is the core output of the unit for evaluating tumor changes during follow-up. It is used to evaluate tumor growth and provide basic data for the subsequent calculation of tumor growth rate OTS and tumor growth prediction value OY. This algorithm unit provides a specific percentage value through the tumor volume change rate OBL, which intuitively shows the growth and reduction of the tumor during the follow-up period. This allows doctors to accurately understand the growth rate of the tumor and then judge the effectiveness of the current treatment; By calculating the tumor volume change rate (OBL), doctors can compare the changes in tumor volume at different follow-up periods and time points to evaluate the effectiveness of the treatment plan. If the tumor volume change rate (OBL) is high, it indicates that the tumor is growing rapidly, and the treatment plan needs to be adjusted, including increasing the drug dosage and using more effective treatment methods. The tumor volume change rate OBL of this algorithm is an important indicator of tumor growth. Its value can be used for scientific research and provide valuable data support for the research in the fields of tumor growth mechanism, treatment method and efficacy evaluation.

[0022] See also Figures 1 to 4 , the calculation formula that fully reflects the tumor growth characteristic unit is as follows: ; in: OTS is the tumor growth rate; T1 is the follow-up time interval, in days. T1 reflects the time interval of the current patient in the current follow-up cycle, that is, the tumor volume at the beginning of the follow-up measurement. start The tumor volume at the end of follow-up was end The time difference between.

[0023] In this embodiment, first This calculation combines the tumor volume change rate OBL and the tumor volume at the beginning of follow-up O start , which is used to reflect the relative growth of the tumor within the follow-up time interval. It takes into account the cardinality effect of the tumor volume, that is, the larger the initial volume of the tumor, the greater its absolute growth under the same tumor volume change rate OBL. The square root part in the formula is an adjustment to the tumor growth rate OTS, which takes into account the relationship between the current tumor volume and growth rate, making the prediction more accurate. As part of the calculation formula for the unit that comprehensively reflects the tumor growth characteristics, this product is added to the volume growth within the follow-up time interval T1, which together constitute the calculation basis of the tumor growth rate OTS. The tumor growth rate OTS is an important indicator for measuring tumor growth rate. This calculation part represents the absolute increase in tumor volume during the follow-up time interval T1, which directly reflects the growth of the tumor during this period of time. As another part of the calculation formula that fully reflects the tumor growth characteristics, this absolute increase is related to Added together, they jointly determine the value of the tumor growth rate OTS, which takes into account the relative change and absolute growth of the tumor volume, providing a basis for predicting the future growth of the tumor; The tumor growth rate OTS calculated and output by this algorithm unit not only takes into account the change in tumor volume, but also combines the follow-up time interval T1, thereby more comprehensively reflecting the growth rate of the tumor. This enables doctors to understand the growth characteristics of the tumor more accurately and provide a basis for formulating personalized treatment plans; By calculating the tumor growth rate (OTS), doctors can predict the future growth trend of the tumor, which helps doctors plan treatment strategies in advance, including determining the timing of surgery and adjusting drug dosage, thereby improving treatment outcomes.

[0024] The tumor growth rate varies among different patients. By calculating the tumor growth rate (OTS), doctors can develop a personalized treatment plan for each patient to ensure the effectiveness and safety of the treatment.

[0025] See also Figures 1 to 4 , the calculation formula for predicting the future growth trend of tumors is as follows: ; in: OY is the predicted value of tumor growth; T2 is the predicted time extension, in days. T2 reflects the time interval between the current follow-up period and the next follow-up period of the current patient, that is, the tumor volume at the end of the follow-up measurement in the current follow-up period. end Tumor volume at the beginning of follow-up until the next follow-up period start The time difference between.

[0026] In this embodiment, the algorithm unit first This calculation part represents the predicted value of the tumor volume after it grows at the tumor growth rate OTS within the prediction time extension T2. ​​It takes into account the impact of the current tumor volume and growth rate on the future volume. As part of the calculation formula for predicting the future growth trend of the tumor, this predicted value is subtracted from the subsequent adjustment item to jointly determine the value of the tumor growth prediction value OY. The tumor growth prediction value OY is used to predict the volume of the tumor in the future. This calculation part is an adjustment to the tumor growth rate OTS, taking into account the relationship between the current tumor volume and the growth rate. It attempts to more accurately predict future changes in tumor volume by considering the relative relationship between the current tumor volume and the growth rate. As an adjustment item in the calculation formula for predicting the future growth trend of the tumor, this calculation part is subtracted from the previous predicted value to jointly determine the final value of the tumor growth prediction value OY. This adjustment item helps to improve the prediction accuracy of the tumor growth prediction value OY. This algorithm unit provides doctors with a specific prediction value through the output tumor growth prediction value OY, which is used to predict the future growth of the tumor. This enables doctors to plan treatment strategies in advance, including deciding whether to perform surgery in advance and adjusting drug treatment plans to ensure that patients obtain the best treatment effect. By understanding the future tumor growth trend, patients can cooperate with treatment more actively. Based on the predicted results of the tumor growth prediction value OY, the hospital can allocate medical resources more reasonably. Specifically, for patients whose tumors are predicted to grow rapidly, surgery and radiotherapy resources can be given priority to ensure that the patients receive timely and effective treatment.

[0027] For example 2, please refer to Figures 1 to 4 , based on the tumor growth prediction value OY and the tumor growth prediction value OY of the previous follow-up period, observe the growth change trend of the tumor growth prediction value OY, and treat the patient according to the change trend; If the predicted tumor growth value OY calculated in the current follow-up period = the tumor volume O measured at the beginning of the next follow-up period start , the tumor growth prediction value OY calculated in the current follow-up period is used as the input value, replacing the tumor volume O at the beginning of the follow-up period measured in the next follow-up period start , and input into the unit for evaluating tumor changes during the follow-up process and the unit for comprehensively reflecting tumor growth characteristics for calculation in the next follow-up cycle; If the tumor growth prediction value OY calculated in the current follow-up period is ≠ the tumor volume O measured at the beginning of the next follow-up period start , the error correction coefficient K is calculated by linear regression analysis, and K is entered into the unit for predicting the future growth trend of the tumor in the next follow-up period for adjustment calculation. The specific calculation formula after adjustment is as follows: ; The specific steps of linear regression analysis are as follows: S1. Set X i is the independent variable, Y i is the dependent variable, and collects the independent variable X i and the dependent variable Y i Observed values ​​and calculate the independent variable X i and the dependent variable Y i The mean of the independent variable X i is the observed value of tumor growth prediction value OY, dependent variable Y i is the observed value of the prediction error C; S2. Use the slope calculation formula and intercept calculation formula to calculate the independent variable X. i and the dependent variable Y i The slope between XL and the intercept J; S3. According to the slope XL and the intercept J, construct the linear equation Y=XL×X+J; S4. In this regression model, the inverse of the slope XL is considered as a candidate value of the error correction coefficient K, that is, K = 1 / XL; The calculation formula of the prediction error C is as follows: ; The slope calculation formula is as follows: ; ; ; n is the total number of observations, and n reflects the total number of observations observed in the previous n cycles for the current patient; X avg is the mean of the independent variable; X1 is the first observed independent variable, X2 is the second observed independent variable, and X3 is the third observed independent variable. n is the nth observed independent variable; Y avg is the mean of the dependent variable; Y1 is the first observed dependent variable, Y2 is the second observed dependent variable, and Y3 is the third observed dependent variable. n is the nth observed dependent variable; The calculation formula for the intercept calculation formula is as follows: .

[0028] In this embodiment, in the loop feedback mechanism, the tumor growth prediction value OY is used as the tumor volume O at the beginning of the follow-up measurement in the next follow-up cycle. start , providing doctors with a dynamic monitoring and prediction tool. With the continuous follow-up, the values ​​of tumor volume change rate OBL, tumor growth rate OTS and tumor growth prediction value OY will be continuously updated, realizing dynamic monitoring and prediction of tumor growth; In the feedback loop, if the predicted tumor growth value OY is not equal to the tumor volume O at the beginning of the follow-up measurement in the next follow-up period, start , that is, there is an error. Doctors can use linear regression analysis to calculate the error correction coefficient K and correct the tumor growth prediction value OY. This step can further improve the accuracy of the prediction and provide doctors with more reliable reference information. This embodiment uses a loop feedback mechanism to enable doctors to timely adjust and optimize treatment plans based on the latest tumor volume change rate OBL, tumor growth rate OTS, and tumor growth prediction value OY, which helps ensure that patients obtain the best treatment effect and improve the effectiveness and safety of treatment; It is worth noting that the introduction of the error correction coefficient K is actually a correction of the tumor growth prediction value OY calculated by predicting the future tumor growth trend unit. This correction is based on the linear regression analysis of historical follow-up data. By finding the difference between the tumor growth prediction value OY and the actual tumor volume O at the beginning of the follow-up measurement in the next follow-up cycle, start The linear relationship between the error between the two values ​​and the tumor growth prediction value OY is used to adjust the tumor growth prediction value OY to make it closer to the actual value. In this way, more accurate tumor growth prediction can be obtained in subsequent tumor follow-up and monitoring, which helps doctors make more informed treatment decisions. By calculating the error correction coefficient K, we can better understand the difference between the predicted value and the actual value of tumor growth, and optimize the tumor follow-up strategy accordingly. Specifically, for patients with large prediction errors C, the frequency of follow-up can be increased to understand the tumor growth more timely, while for patients with small prediction errors, the number of follow-up visits can be appropriately reduced to reduce the burden on patients and the waste of medical resources. Tumor growth and changes vary from person to person, and different patients require different treatment plans. By calculating the error correction coefficient K, we can more accurately predict the tumor growth of each patient, thereby developing a more personalized treatment plan for the patient. This personalized treatment can not only improve the treatment effect, but also reduce unnecessary side effects and complications. In summary, calculating the error correction coefficient K by combining the unit for evaluating tumor changes during follow-up, the unit for comprehensively reflecting tumor growth characteristics, the unit for predicting future tumor growth trends, and the unit for error analysis and correction has many beneficial effects, including improving the accuracy of tumor growth prediction, optimizing tumor follow-up strategies, enhancing the effect of personalized treatment, and promoting the in-depth development of tumor research. These effects not only help to improve patients' treatment effects and quality of life, but also can bring new breakthroughs and developments to the field of tumor research and treatment. The most important thing is to provide efficient and accurate processing methods for the follow-up data of hepatobiliary tumors.

[0029] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hepatobiliary tumor follow-up data processing service system, characterized in that: The specific implementation steps are as follows: Step 1: Use the data collection module to collect the growth and changes of hepatobiliary tumors in the current follow-up period of the current patient; Step 2: Using the data prediction processing module, the tumor volume change rate OBL, the tumor growth rate OTS, and the tumor growth prediction value OY are calculated and output in sequence. Based on the tumor growth prediction value OY, the change trend of the current patient's hepatobiliary tumor is observed, and basic data is provided for follow-up diagnosis and treatment; The data prediction and processing module includes a unit for evaluating tumor changes during follow-up, a unit for comprehensively reflecting tumor growth characteristics, a unit for predicting future tumor growth trends, and a unit for error analysis and correction; Step 3: In the next follow-up period, the current patient uses the data prediction processing module and uses the tumor growth prediction value OY of the previous follow-up period as the tumor volume O at the beginning of the next follow-up period. start The input values ​​form the continuous calculation of follow-up; Step 4: Before each follow-up calculation, the error analysis and correction unit is used to compare the tumor growth prediction value OY of the previous follow-up cycle with the tumor volume O at the beginning of the current follow-up cycle. start The existing errors are analyzed and corrected, and acted on the unit for predicting the future growth trend of the tumor to perform adjustment calculations.

2. A hepatobiliary tumor follow-up data processing service system according to claim 1, characterized in that: The equipment used by the data collection module includes a server and a database management system; The equipment used by the data prediction processing module includes data analysis software and a workstation.

3. A hepatobiliary tumor follow-up data processing service system according to claim 2, characterized in that: The calculation formula for evaluating the tumor change unit during the follow-up is as follows: ； in: OBL is the rate of change of tumor volume; O end is the tumor volume at the end of follow-up, O end Reflects the tumor volume of the current patient at the end of the current follow-up period; O start is the tumor volume at the beginning of follow-up, O start Reflects the tumor volume of the current patient at the beginning of the current follow-up period.

4. A hepatobiliary tumor follow-up data processing service system according to claim 3, characterized in that: The calculation formula for the unit that fully reflects the tumor growth characteristics is as follows: ； in: OTS is the tumor growth rate; T1 is the follow-up time interval, in days. T1 reflects the time interval of the current patient in the current follow-up cycle, that is, the tumor volume at the beginning of the follow-up measurement. start The tumor volume at the end of follow-up was end The time difference between.

5. A hepatobiliary tumor follow-up data processing service system according to claim 4, characterized in that: The calculation formula for predicting the future growth trend of the tumor is as follows: ； in: OY is the predicted value of tumor growth; T2 is the predicted time extension, in days. T2 reflects the time interval between the current follow-up period and the next follow-up period of the current patient, that is, the tumor volume at the end of the follow-up measurement in the current follow-up period. end Tumor volume at the beginning of follow-up until the next follow-up period start The time difference between.

6. A hepatobiliary tumor follow-up data processing service system according to claim 5, characterized in that: Based on the tumor growth prediction value OY and the tumor growth prediction value OY of the previous follow-up period, observing the growth change trend of the tumor growth prediction value OY, and treating the patient according to the change trend; If the predicted tumor growth value OY calculated in the current follow-up period = the tumor volume O measured at the beginning of the next follow-up period start , the tumor growth prediction value OY calculated in the current follow-up period is used as the input value, replacing the tumor volume O at the beginning of the follow-up period measured in the next follow-up period start , and input into the unit for evaluating tumor changes during the follow-up process and the unit for comprehensively reflecting tumor growth characteristics for calculation in the next follow-up cycle; If the tumor growth prediction value OY calculated in the current follow-up period is ≠ the tumor volume O measured at the beginning of the next follow-up period start , the error correction coefficient K is calculated by linear regression analysis, and K is entered into the unit for predicting the future growth trend of the tumor in the next follow-up period for adjustment calculation. The specific calculation formula after adjustment is as follows: 。 7. A hepatobiliary tumor follow-up data processing service system according to claim 6, characterized in that: The specific steps of the linear regression analysis are as follows: S1. Set X i is the independent variable, Y i is the dependent variable, and collects the independent variable X i and the dependent variable Y i Observed values ​​and calculate the independent variable X i and the dependent variable Y i The mean of the independent variable X i is the observed value of tumor growth prediction value OY, dependent variable Y i is the observed value of the prediction error C; S2. Use the slope calculation formula and intercept calculation formula to calculate the independent variable X. i and the dependent variable Y i The slope between XL and the intercept J; S3. According to the slope XL and the intercept J, construct the linear equation Y=XL×X+J; S4. In this regression model, the reciprocal of the slope XL is considered as a candidate value of the error correction coefficient K, that is, K=1 / XL.

8. A hepatobiliary tumor follow-up data processing service system according to claim 7, characterized in that: The calculation formula of the prediction error C is as follows: ； The calculation formula of the slope calculation formula is as follows: ； ； ； n is the total number of observations, and n reflects the total number of observations observed in the previous n cycles for the current patient; X avg is the mean of the independent variable; X1 is the first observed independent variable, X2 is the second observed independent variable, and X3 is the third observed independent variable. n is the nth observed independent variable; Y avg is the mean of the dependent variable; Y1 is the first observed dependent variable, Y2 is the second observed dependent variable, and Y3 is the third observed dependent variable. n is the nth observed dependent variable; The calculation formula of the intercept calculation formula is as follows: 。

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