Prediction method in gastric cancer peritoneal metastasis operation, electronic equipment and program product

Abstract

The invention discloses a gastric cancer peritoneal metastasis intra-operative prediction method, which comprises the following steps of: establishing a column diagram intra-operative prediction model, and assisting an operation decision through the prediction model. The model prediction factors of the column plot intra-operative prediction model comprise Lauren typing, clinical T stage, peritoneal nodule distribution area and the ratio of neutrophils to lymphocytes. According to the prediction model in the column chart operation, the scale score range of the column chart is 0-100, including a Lauren type, including an intestinal type, a mixed type and a diffuse type, a clinical T stage, including T1-3, T4a and T4b, a peritoneal nodule distribution region, including Area a, Area b and Area c, a ratio of neutrophil to lymphocyte, including less than 2.88 or greater than or equal to 2.88, a total score, the score range is 0-400, and the peritoneal metastasis probability range is 0.1-0.95.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical big data, and in particular relates to an intraoperative prediction method, electronic equipment and program product for peritoneal metastasis of gastric cancer. Background Art

[0002] Peritoneal metastasis is one of the main types of metastasis of gastric cancer and has a serious impact on the patient's prognosis. When suspicious peritoneal nodules are found during surgery, doctors need to quickly decide whether to perform gastrectomy. This decision is based on the results of frozen pathological examination of peritoneal nodules, but these results may be negative, positive, or uncertain. A negative result may mean that no cancer cells are found, but there is a risk of false negatives; a positive result clearly indicates the presence of cancer cells; an uncertain result requires further analysis. In this case, doctors need to make the best treatment decision based on limited information, which not only involves whether to perform gastrectomy, but also affects the patient's prognosis and quality of life. Summary of the invention

[0003] The disclosed embodiment establishes an intraoperative prediction model for gastric cancer peritoneal metastasis by combining clinical pathological information and intraoperative pathological evaluation to assist surgeons in making more accurate surgical decisions during surgery. By analyzing Lauren classification, clinical T stage, peritoneal nodule distribution area, neutrophil / lymphocyte count ratio (NLR) and CA125, a comprehensive prediction model is proposed, which shows significant advantages in improving the accuracy of surgical decisions and patient survival rate.

[0004] The disclosed embodiments establish an intraoperative prediction model for gastric cancer peritoneal metastasis through univariate and multivariate analysis, including Lauren classification, clinical T stage, peritoneal nodule distribution area and neutrophil / lymphocyte count ratio to predict gastric cancer peritoneal metastasis. Alternatively, Lauren classification, peritoneal nodule distribution area and CA125 are used as an aid to predict positive paraffin section results when frozen pathology examination results are negative or uncertain. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0006] Figure 1 A schematic diagram of a nomogram for intraoperative prediction of gastric cancer peritoneal metastasis according to one embodiment of the present invention.

[0007] Figure 2 ROC curve diagram of the nomogram prediction model according to one embodiment of the present invention.

[0008] Figure 3A schematic diagram of the correlation between the total score of the nomogram and peritoneal metastasis according to one embodiment of the present invention. DETAILED DESCRIPTION

[0009] According to one or more embodiments, an intraoperative prediction model for gastric cancer peritoneal metastasis based on clinical pathological parameters is provided, which is used to provide support for surgeons in intraoperative decision-making, thereby improving the treatment and prognosis of gastric cancer patients.

[0010] In the construction of this prediction model, sample data of 318 gastric cancer patients from January 2015 to July 2021 were included. By examining the correlation between frozen and paraffin pathology of peritoneal nodules, predictive factors for positive paraffin section results were determined and a nomogram was constructed. The clinical pathological data of all patients were collected from the hospital's electronic medical record system. The study was approved by the hospital's ethics committee and conformed to the principles of the Declaration of Helsinki.

[0011] The inclusion criteria for the study were: (1) patients with gastric cancer confirmed by histology; (2) suspicious peritoneal nodules found during surgery. The exclusion criteria were: (1) lack of necessary clinical pathological data.

[0012] The collected data included patients' basic information (age, gender), clinicopathological characteristics (tumor size, location, and stage), intraoperative findings (distribution and size of peritoneal nodules), pathological results (frozen section (FS) and paraffin section (PS) results), and postoperative survival.

[0013] Univariate and multivariate logistic regression analyses were performed to identify predictors of positive paraffin section results. A nomogram prediction model was constructed based on these predictors, and the ROC curve was used to evaluate the predictive accuracy of the model. Survival analysis was performed using the Kaplan-Meier method and Log-rank test, and the Cox proportional hazards model was used to estimate the effect of gastrectomy on survival.

[0014] Among 70 patients with negative FS, 59 (84.3%) had a consistent negative PS result, whereas 11 (15.7%) had a positive PS result. Among 49 patients with indeterminate FS results, 46 (93.9%) and 3 (6.1%) had positive and negative PS results, respectively. All 95 patients with positive FS had positive PS results. A nomogram predicting a positive PS result was developed based on Lauren classification, peritoneal nodule distribution area, and CA125. For patients with negative FS, gastrectomy improved survival compared with no gastrectomy (HR 0.26, 95% CI 0.11–0.62; P = 0.0012).

[0015] The disclosed embodiments establish an intraoperative prediction model for peritoneal metastasis of gastric cancer through univariate and multivariate analysis. Considerations include constructing a prediction model based on Lauren classification, distribution area of ​​peritoneal nodules, and CA125. Due to the combination of multiple clinical pathological parameters, a more comprehensive intraoperative prediction model is provided through comprehensive analysis. This model assists surgeons in making more accurate decisions during surgery. By combining multiple clinical pathological parameters, the model is able to predict the risk of peritoneal metastasis in patients with gastric cancer, thereby guiding surgical strategies. For operable gastric cancer patients with negative frozen pathology results of peritoneal nodules, the rationale for gastrectomy can be demonstrated. For patients with positive and uncertain frozen pathology results, gastrectomy is not recommended unless performed as a palliative surgery.

[0016] Univariate analysis showed that Lauren classification, clinical T stage, peritoneal nodule distribution area, and NLR were significant predictors of positive paraffin section results, i.e., peritoneal metastasis. Multivariate analysis further confirmed the independent predictive value of these factors. The nomogram prediction model constructed based on the above predictive factors showed good predictive accuracy, with an AUC value of 0.784. The calibration curve of the model showed good consistency between the predicted results and the actual results.

[0017] For patients with negative FS, the median survival time was not reached for those who underwent gastrectomy, whereas it was 14 months for those who did not undergo gastrectomy. Gastrectomy significantly improved the survival of patients with negative FS (HR 0.26, 95% CI 0.11–0.62; P = 0.0012). For patients with indeterminate and positive FS, gastrectomy did not show a survival benefit.

[0018] Therefore, the beneficial effects of the embodiments of the present disclosure include:

[0019] Improving the accuracy of surgical decision-making - improving the prediction accuracy of peritoneal metastasis of gastric cancer by combining multiple clinical pathological information.

[0020] Personalized medicine - Provide customized surgical decisions for each patient to improve treatment outcomes.

[0021] Reduce unnecessary surgeries - Through the prediction model, unnecessary gastrectomies can be reduced in patients with negative prediction results.

[0022] Improved survival - For FS-negative patients, gastrectomy can significantly improve survival.

[0023] According to one or more embodiments, a method for intraoperative prediction of peritoneal metastasis of gastric cancer is provided. When peritoneal nodules are found in gastric cancer patients during surgery, an intraoperative prediction model for peritoneal metastasis of gastric cancer is established based on the patient's clinical pathological information to help surgeons make surgical decisions to decide whether to perform gastrectomy. The prediction method uses univariate and multivariate analysis to explore the risk factors for postoperative routine pathology of peritoneal nodules in gastric cancer patients, establishes a nomogram prediction model, determines the cutoff value based on the ROC curve, and divides the patients into low-risk and high-risk groups.

[0024] The process of constructing the prediction model is as follows.

[0025] First, 316 gastric cancer patients with postoperative routine pathological results of peritoneal nodules in Zhongshan Hospital were included. Through univariate and multivariate analysis, it was found that the risk factors for positive postoperative routine pathological results of peritoneal nodules in gastric cancer patients were Lauren type, clinical T stage (cT), peritoneal nodule distribution, and neutrophil / lymphocyte count ratio (NLR). A nomogram prediction model was established based on these four indicators.

[0026] The ROC curves of the nomogram model and the above four indicators showed that the accuracy of Nomogram (AUC: 0.784, 95% CI 0.719-0.850) in predicting peritoneal metastasis was better than Lauren classification (AUC: 0.649, 95% CI 0.579-0.719; P < 0.001), cT stage (AUC: 0.621, 95% CI 0.558-0.684; P < 0.001), peritoneal nodule distribution area (AUC: 0.687, 95% CI: 0.628-0.746; P < 0.001) and NLR (AUC: 0.576, 95% CI: 0.520-0.631; P < 0.001). The total nomogram score of patients with peritoneal metastasis was significantly higher than that of patients without peritoneal metastasis. According to the ROC curve, the critical value was determined to be 120, and the patients were divided into a low-risk group (score < 120) and a high-risk group (score ≥ 120). The sensitivity of the nomogram was 93.0%, the false negative rate was 7.0%, the specificity was 51.4%, and the false positive rate was 48.6%. The negative predictive value of the nomogram was 78.7%, and the positive predictive value was 79.0%.

[0027] like Figure 1 As shown, the English terms in the figure include:

[0028] 1.Points

[0029] 2. Lauren Type-Lauren classification: Based on the histological morphological characteristics, gastric cancer can be divided into intestinal type, diffuse type and mixed type.

[0030] 3.cT-clinical T staging: indicates the primary size and extent of the tumor. T1-3 means that the tumor is confined to the gastric serosa, T4a means that the tumor has invaded the gastric serosa, and T4b means that the tumor has invaded adjacent structures or organs outside the stomach.

[0031] 4. Nodule distribution-area of ​​peritoneal nodules: Area a, b, and c represent the areas of peritoneal nodule distribution, which correspond to the areas indicated by P1a, P1b, and P1c in the 15th edition of the Japanese "Rules for the Management of Gastric Cancer". P1a: Peritoneal metastasis is limited to the stomach, greater omentum, lesser omentum, anterior lobe of transverse mesocolon, pancreatic capsule, and spleen; P1b: There is peritoneal metastasis in the upper abdominal peritoneum (the parietal peritoneum on the head side of the umbilicus, the visceral peritoneum on the head side of the transverse colon); P1c: There is peritoneal metastasis in the middle and lower abdominal peritoneum.

[0032] 5.NLR-Neutrophil to Lymphocyte Ratio: An indicator used to assess the patient's inflammation and immune response levels.

[0033] 6.Total Points: The total score calculated based on the above indicators.

[0034] 7. Probability of PM-Probability of peritoneal metastasis: The possibility of peritoneal metastasis predicted based on the total score.

[0035] Should Figure 1 It is a scoring system used to predict the probability of peritoneal metastasis in patients with gastric cancer. The system calculates the total score by comprehensively considering Lauren classification, clinical T stage (cT) of the tumor, peritoneal nodule distribution, and neutrophil-to-lymphocyte ratio (NLR), and predicts the probability of peritoneal metastasis.

[0036] Lauren classification: intestinal type, mixed type and diffuse type correspond to different scores, reflecting the differences in biological behavior and prognosis of different types of gastric cancer.

[0037] cT staging: The distinction between T1-3 and T4a / T4b reflects the depth of tumor invasion. T4 stage tumors are more likely to be accompanied by peritoneal metastasis.

[0038] Distribution area of ​​peritoneal nodules: The distinction between Area a, b, and c is directly related to the distribution area of ​​peritoneal nodules. Area c means that the distribution area of ​​peritoneal nodules is farther away from the stomach, indicating a higher risk of peritoneal metastasis of gastric cancer.

[0039] NLR: The level of NLR is related to the patient's inflammation and immune response level. NLR ≥ 2.88 is usually associated with a higher risk of peritoneal metastasis of gastric cancer.

[0040] By converting these clinical pathological features into scores and accumulating them to obtain a total score, the probability of peritoneal metastasis in patients can be predicted. The higher the total score, the higher the predicted probability of peritoneal metastasis, which may prompt doctors to take more aggressive surgical treatment measures. Through the prediction model provided by the embodiments of the present disclosure, when suspicious peritoneal nodules are found in gastric cancer patients during surgery, surgeons can use the model to predict the probability of peritoneal metastasis to help with surgical decision-making.

[0041] Specifically, the specific steps for constructing an intraoperative prediction model for gastric cancer peritoneal metastasis include:

[0042] S101. Data collection and patient inclusion. Gastric cancer patients with suspicious peritoneal nodules found during surgery were selected as research subjects. Basic information of the patients, clinical pathological characteristics (including tumor size, location, and stage), intraoperative findings (distribution and size of peritoneal nodules), and pathological results (frozen section and paraffin section results) were collected.

[0043] S102. First, univariate logistic regression analysis was used to identify potential predictive factors associated with a positive result for peritoneal metastasis. Then, the significant predictive factors in the univariate analysis were further subjected to multivariate logistic regression analysis to identify independent predictive factors.

[0044] S103. Based on the independent predictive factors determined by multivariate analysis, a nomogram prediction model was constructed. The ROC curve (receiver operating characteristic curve) was used to evaluate the prediction accuracy of the model and determine the optimal cutoff value to distinguish between the low-risk group and the high-risk group.

[0045] S104. The consistency between the model prediction results and the actual results was evaluated by calibration curve. The Kaplan-Meier method and Log-rank test were used to evaluate the survival differences of patients in different risk groups.

[0046] S105. Explain the prediction results of the model, including sensitivity, specificity, false positive rate, false negative rate, and positive predictive value and negative predictive value. Apply the model in clinical practice to help surgeons make intraoperative decisions on whether to perform gastrectomy.

[0047] S106. Validate the predictive performance of the model in more patient groups to ensure the universality and reliability of the model. Continuously update and optimize the model based on new clinical data and research results.

[0048] The disclosed embodiment verifies the accuracy of the prediction by comparing the output of the prediction model. Figure 2 and Figure 3 shown.

[0049] like Figure 2 As shown, Figure 2 The English terms in include:

[0050] 1.Sensitivity: refers to the ability of the test to correctly identify positive cases, that is, the true positive rate.

[0051] 2.1-Specificity-1-Specificity: Specificity refers to the ability of the test to correctly identify negative cases, while 1-specificity refers to the false positive rate.

[0052] 3. Lauren type-Lauren classification: Based on histological morphological characteristics, gastric cancer can be divided into intestinal type, diffuse type and mixed type.

[0053] 4.cT-clinical T staging: indicates the primary size and extent of the tumor. T1-3 means that the tumor is confined to the gastric serosa, T4a means that the tumor has invaded the gastric serosa, and T4b means that the tumor has invaded adjacent structures or organs outside the stomach.

[0054] 5. Nodule distribution-area of ​​peritoneal nodules: a, b, c represent the area of ​​peritoneal nodule distribution, which correspond to the areas indicated by P1a, P1b, and P1c in the 15th edition of the Japanese "Rules for the Management of Gastric Cancer". P1a: Peritoneal metastasis is limited to the stomach, greater omentum, lesser omentum, anterior lobe of transverse mesocolon, pancreatic capsule, and spleen; P1b: There is peritoneal metastasis in the upper abdominal peritoneum (the parietal peritoneum on the head side of the umbilicus, the visceral peritoneum on the head side of the transverse colon); P1c: There is peritoneal metastasis in the middle and lower abdominal peritoneum.

[0055] 6.NLR-Neutrophil to Lymphocyte Ratio: An indicator used to assess the patient's inflammation and immune response levels.

[0056] 7.Nomogram-Nomogram.

[0057] 8. Reference line-reference line, diagonal line in the figure: In the ROC curve, the diagonal line represents the sensitivity and specificity of random guessing. The ROC curve of any effective prediction model should be above this line.

[0058] Figure 2 The receiver operating characteristic (ROC) curve is used to evaluate the performance of different prediction models or indicators in predicting peritoneal metastasis (PM) of gastric cancer. The ROC curve shows the performance of the test by plotting the relationship between sensitivity (true positive rate) and 1-specificity (false positive rate). The larger the area under the curve (AUC), the stronger the predictive ability of the test. Lauren type, cT, Nodule distribution, NLR, these are separate predictive indicators, and their ROC curves show their respective sensitivity and specificity in predicting peritoneal metastasis. Nomogram is a comprehensive prediction model built based on multiple variables (including Lauren type, cT stage, peritoneal nodule distribution area, and NLR). The ROC curve of the nomogram model shows its performance in predicting peritoneal metastasis, which is usually more accurate than a single indicator.

[0059] like Figure 3 The English terms include:

[0060] 1.Nomogram total points-Nomogram total points: The total score calculated by the nomogram model is used to predict disease risk.

[0061] 2.PM-Peritoneal Metastasis: refers to the spread of cancer cells from the primary tumor to the peritoneum.

[0062] 3.No PM-No peritoneal metastasis: means that no cancer cells were detected to spread to the peritoneum.

[0063] 4. Low-risk group: A group of patients with lower risk according to the total score of the nomogram.

[0064] 5. High-risk group: A group of patients with higher risk according to the total score of the nomogram.

[0065] Figure 3The relationship between the total score of the nomogram and peritoneal metastasis (PM) is shown, and the peritoneal metastasis of patients in different score segments is shown by risk grouping. The left figure shows the distribution of the total score of the nomogram for patients with or without peritoneal metastasis. It can be seen that the total score of patients with peritoneal metastasis (PM) is significantly higher than that of patients without peritoneal metastasis (No PM), and the difference is statistically significant (**** indicates that the P value is very small, usually less than 0.0001). The middle figure shows that 93% of patients with peritoneal metastasis (PM) are in the high-risk group, and the test sensitivity is as high as 93%. The right figure shows the proportion of patients with or without peritoneal metastasis in the low-risk group (total score <120) and the high-risk group (total score ≥120). 78.7% of patients in the low-risk group had no peritoneal metastasis, while 79.0% of patients in the high-risk group had peritoneal metastasis, which further confirmed the effectiveness of the total score of the nomogram in predicting the risk of peritoneal metastasis.

[0066] Figure 3 The results showed that the total score of the nomogram can be used as an effective tool to predict the risk of peritoneal metastasis in patients with gastric cancer, helping doctors to make risk assessment and treatment decisions in clinical practice.

[0067] It should be understood that in the embodiments of the present invention, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0068] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0070] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intraoperative prediction of gastric cancer peritoneal metastasis, characterized in that: The method includes establishing a nomogram intraoperative prediction model, and using the prediction model to assist surgical decision-making.

2. The prediction method according to claim 1, characterized in that: The nomogram intraoperative prediction model, the model prediction factors include Lauren classification, clinical T stage, peritoneal nodule distribution area, and neutrophil-to-lymphocyte ratio.

3. The prediction method according to claim 2, characterized in that: The nomogram intraoperative prediction model, the nomogram scale score range is 0 to 100, including: Lauren classification includes intestinal type, mixed type and diffuse type. Clinical T stage, including T1-3, T4a and T4b, The distribution areas of peritoneal nodules include Area a, Area b, and Area c. Neutrophil-to-lymphocyte ratio, including less than 2.88 or greater than or equal to 2.88, total score, score range 0 to 400, The probability of peritoneal metastasis ranges from 0.1 to 0.

95.

4. The prediction method according to claim 1, characterized in that: The process of constructing the prediction model during the nomogram procedure includes: Univariate logistic regression analysis was first used to identify potential predictive factors associated with a positive result for peritoneal metastasis; Then, the significant predictive factors in the univariate analysis were further subjected to multivariate logistic regression analysis to identify independent predictive factors; Based on the independent predictive factors identified by multivariate analysis, a nomogram prediction model was constructed.

5. The prediction method according to claim 4, characterized in that: The ROC curve was used to evaluate the prediction accuracy of the nomogram intraoperative prediction model and determine the optimal cutoff value to distinguish between the low-risk group and the high-risk group.

6. The prediction method according to claim 5, characterized in that: The Kaplan-Meier method and Log-rank test were used to evaluate the survival differences among patients in different risk groups.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor runs the computer program to implement the method according to any one of claims 1 to 6.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

Images