Preoperative evaluation and R0 excision prediction model and prediction method for ovarian cancer initial tumor cell deduction

Abstract

The invention relates to the field of medical diagnosis, in particular to a preoperative evaluation and R0 excision prediction model and prediction method for ovarian cancer initial tumor cell deduction. The preoperative evaluation and R0 resection prediction model comprises a data acquisition module, a preoperative evaluation module and an R0 resection prediction module. The preoperative evaluation module is used for assigning and calculating tumor diameter data according to tumor lesion sizes to obtain lesion scores, the R0 resection prediction module is used for calculating R0 resection prediction scores, and the R0 resection prediction scores are obtained by multiplying six factors by corresponding assignments and adding the six factors. Through individualized evaluation, the operation R0 excision rate is improved, so that the overall ovarian cancer treatment effect is improved, and the living quality and prognosis of a patient are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis, and in particular to a preoperative evaluation and R0 resection prediction model and prediction method for initial cytoreductive surgery for ovarian cancer. Background Art

[0002] Ovarian cancer is the most lethal gynecological malignancy. Ultra-radical surgery with the goal of achieving macroscopically free remnant (R0) is the cornerstone of treatment for advanced ovarian cancer. Systematic and standardized preoperative evaluation is an important guide to improving R0 resection. The traditional preoperative evaluation model relies too much on the subjective judgment of the surgeon and lacks objective basis, making it difficult to choose the most appropriate treatment plan for patients.

[0003] Currently, there are two initial treatments for advanced ovarian cancer. Initial cytoreductive surgery (PDS) is the first choice. When PDS cannot achieve a satisfactory level of cytoreduction and / or the patient has perioperative high-risk factors, neoadjuvant chemotherapy (NACT) + intermittent cytoreductive surgery (IDS) is an alternative. However, most patients with advanced ovarian cancer are postmenopausal, older, have multiple underlying diseases, and generally have different tolerance conditions. The metastasis of abdominal and pelvic tumors is complex, making it difficult to choose an initial treatment plan. Gynecological oncology experts at home and abroad are committed to exploring precise preoperative evaluation methods in order to find sensitive, specific, and accurate evaluation methods to help gynecological oncologists develop precise initial treatment plans for patients with advanced ovarian cancer.

[0004] The evaluation before the initial treatment of advanced ovarian cancer involves factors such as the patient's general tolerance, test indicators, tumor markers, and imaging examinations. However, it still mainly relies on the doctor's experience and subjective judgment. Classic evaluation methods before the initial treatment of ovarian cancer include Fagotti score, Sudan score, and Mayo Clinic classification model. Fagotti score is a preoperative laparoscopic exploration to evaluate the metastasis of the whole abdominal cavity tumor and determine whether the tumor can be satisfactorily reduced. The advantage is that the tumor metastasis can be visually evaluated, but the disadvantage is that the evaluation is not comprehensive. If there is adhesion in the upper abdominal area, the condition of the lower organs cannot be evaluated, and laparoscopy is still an invasive examination, which needs to be completed under general anesthesia in the operating room, and the actual application is limited. Sudan score is a combination of 3 clinical factors and 8 imaging scores, and the score results correspond to the possibility of R0 resection. Mayo Clinic classification only divides surgery into high-risk types based on albumin, age, and FIGO staging. The above evaluation methods are all for the evaluation of surgical resectability, and they are not fully applicable to the actual clinical situation in China.

[0005] Therefore, an individualized and comprehensive evaluation method is urgently needed to provide more accurate preoperative evaluation and R0 resection prediction for ovarian cancer patients. Summary of the invention

[0006] The purpose of the present invention is to propose a preoperative evaluation and R0 resection prediction model and prediction method for initial tumor cell reduction surgery for ovarian cancer.

[0007] The present invention proposes a preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer, comprising:

[0008] Data acquisition module: used to obtain tumor diameter data of anatomical sites related to ovarian cancer and whether there is ascites;

[0009] Preoperative evaluation module: used to calculate the lesion score by assigning values ​​to the tumor diameter data according to the size of the tumor lesion;

[0010] R0 resection prediction module: used to calculate the R0 resection prediction score, including: multiplying the 6 factors by their corresponding assigned values ​​and adding them together to obtain the R0 resection prediction score; the 6 factors include: CA125 ≥ 560 U / mL, PNI ≤ 45.7, spleen diaphragm surface lesion score, liver and kidney crypt lesion score, small intestinal mesentery lesion score, and total upper abdominal lesion score.

[0011] The present invention proposes a method for predicting R0 resection of initial tumor cell reduction surgery for ovarian cancer based on the above model. The critical score of the model is 11. The level of R0 resection is judged by the level of R0 resection prediction score. When the R0 resection prediction score is greater than 11, it indicates a low possibility of R0 resection. When the model score is less than or equal to 11, it indicates a high possibility of R0 resection.

[0012] The present invention proposes a computer-readable storage medium, characterized in that it is used to store computer instructions, and when the computer instructions are executed by a processor, the preoperative evaluation and R0 resection prediction model of the above-mentioned initial tumor cell reduction surgery for ovarian cancer are completed.

[0013] The present invention proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the preoperative evaluation of the initial tumor cell reduction surgery for ovarian cancer and the R0 resection prediction model are implemented.

[0014] The technical solution provided by the present invention has the following technical advantages compared with the prior art:

[0015] The preoperative evaluation model provided by the present invention can objectively evaluate the patient's preoperative imaging examination data and clinical information, achieve correct diversion of patients, and enable patients to receive appropriate treatment plans in an individualized manner, thereby improving the R0 rate.

[0016] The present invention provides accurate surgical decision-making basis through individualized evaluation, correctly diverts patients, and improves the surgical R0 resection rate; improves the overall effect of ovarian cancer treatment, and improves the patient's quality of life and prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a comparative experimental result diagram of Example 1;

[0018] Figure 2 This is a diagram of the experimental results of Example 2. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the specification.

[0020] The embodiment of the present invention relates to a preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer, including:

[0021] Data acquisition module: used to obtain tumor diameter data of anatomical sites related to ovarian cancer and whether there is ascites;

[0022] Preoperative evaluation module: used to calculate the lesion score by assigning values ​​to the acquired tumor diameter data according to the size of the tumor lesion;

[0023] R0 resection prediction module: used to calculate the R0 resection prediction score, including: multiplying the 6 factors by their corresponding assigned values, and adding them together to obtain the R0 resection prediction score; the 6 factors include: CA125 ≥ 560 U / mL, PNI ≤ 45.7, spleen diaphragm surface lesion score, liver and kidney crypt lesion score, small intestinal mesentery lesion score, and total upper abdominal lesion score.

[0024] Prognostic Nutritional Index (PNI) is an important indicator for evaluating the nutritional status and prognosis of patients. It is calculated based on serum albumin and peripheral blood lymphocyte count. The formula is: PNI = serum albumin (g / L) + 5 × total number of peripheral blood lymphocytes (×10 9 / L).

[0025] The preoperative evaluation module in the embodiment of the present invention, by evaluating the complexity of the operation and the risk of complications, formulates a more reasonable and complete initial treatment plan and surgical plan, thereby reducing surgical risks and complications. At present, the basis for surgical decision-making mainly relies on the experience of the surgeon, which is relatively subjective and lacks objective evaluation indicators. Using the preoperative evaluation system of the present invention, neoadjuvant chemotherapy can be recommended for patients who are assessed to be unable to undergo R0 resection, while initial tumor cell reduction surgery can be recommended for patients who can undergo R0 resection. Correct diversion of patients can be achieved, so that patients can receive appropriate treatment plans in a personalized manner, thereby improving the R0 resection rate.

[0026] In the data acquisition module of the embodiment of the present invention, the anatomical sites related to ovarian cancer are determined by a multidisciplinary discussion method, including: liver diaphragm surface, spleen diaphragm surface, central tendon, liver capsule, liver hilum, liver ligament, gallbladder fossa, hepatorenal recess, liver parenchyma, stomach surface, lesser omentum, spleen capsule, spleen and stomach space, liver area, spleen area, transverse colon area, small intestinal mesentery, small intestinal surface, ileocecal part, sigmoid colon, left paracolic ditch peritoneum, right paracolic ditch peritoneum, rectum, Douglas fossa, bladder peritoneal reflection, left or right attachment, renal vein lymph nodes, renal vein lymph nodes, left or right pelvic lymph nodes. The above anatomical sites are not only common metastatic sites of ovarian cancer, but also factors that may affect the realization of R0 resection. In order to better assess the tumor burden and predict the surgical process. In order to avoid subjective bias, the same experienced radiologist reads the film and evaluates each site. The area with lesions in the image assessment is defined as a positive area, and the sum of the number of positive areas is defined as the overall tumor load score (Tumor load score, TLS).

[0027] The embodiment of the present invention further provides a method for calculating the value of the tumor lesion size by calculating the lesion score according to the tumor diameter at the site, including:

[0028] 0 < When the tumor diameter is < 0.5 cm, the lesion score is 1 point;

[0029] 0.5≤When the tumor diameter is <5 cm, the lesion score is 2 points;

[0030] Tumor diameter ≥ 5 cm or confluent lesions: lesion score 3 points;

[0031] If there is ascites, the lesion score is 1 point; if there is no ascites, the lesion score is 0 point.

[0032] Specifically, data on tumor diameter are obtained through magnetic resonance imaging (MRI).

[0033] The scoring system also takes into account the spatial distribution of tumor burden. The abdomen is divided into three regions: upper, middle, and lower, and the sum of the scores of each region is calculated and included in the system. The upper abdomen includes the diaphragmatic peritoneum, liver, stomach, and spleen. The lower abdomen includes the pelvis and rectum. The rest of the abdomen is divided into the middle part, except for the lymph nodes. The specific preoperative evaluation system is shown in Table 1.

[0034] Table 1

[0035]

[0036]

[0037] The above table can be used to objectively and comprehensively evaluate the tumor burden of the entire abdomen.

[0038] During the operation, the same table items were used, and the diameter of the lesion was directly observed with the naked eye to obtain intraoperative assessment data as the gold standard and the outcome events.

[0039] In the preoperative evaluation and R0 resection prediction model of the embodiment of the present invention, a value, namely a "prediction score", is set for each factor. This value is determined based on the odds ratio (OR value) of each factor. Specifically, a basic preoperative evaluation score is first set for each clinical or imaging standard. This basic score is multiplied by its corresponding OR value to calculate the prediction score of the factor, and the sum of the prediction scores of the 6 factors is the R0 resection prediction model score.

[0040] Specifically: when CA125 ≥ 560, the value is assigned to 2 points; when PNI ≤ 45.7, the value is assigned to 2 points; the assignment method for the spleen diaphragm surface is: the lesion score of the tumor at the spleen diaphragm surface (0, 1, 2, 3) × 3; the assignment method for the hepatorenal recess is: the lesion score of the tumor at the hepatorenal recess (0, 1, 2, 3) × 2; the assignment method for the small intestinal mesentery is: the lesion score of the tumor at the small intestinal mesentery (0, 1, 2, 3) × 2; the assignment method for the total score of the upper abdominal area lesions is: the total score of the upper abdominal area lesions (0, 1, 2, 3) × 1.

[0041] Among them, the total score of upper abdominal lesions is the sum of the lesion scores of the liver diaphragm surface, spleen diaphragm surface, central tendon, liver capsule, liver hilum, liver ligament, gallbladder fossa, hepatorenal recess, liver parenchyma, gastric surface, lesser omentum, spleen capsule and spleen-stomach space obtained by the data acquisition module.

[0042] For example, for clinical parameters, if the patient's CA-125 level is ≥560U / mL or PNI ≤45.7, each criterion will receive 2 points. The total prediction score for each patient is the sum of the prediction scores of all factors. Based on the patient's total prediction score, it is possible to predict their likelihood of achieving R0 resection. The higher the prediction score, the lower the likelihood that the patient will achieve complete cytoreduction. The Youden Index was calculated using the 6-factor scores and ROC curve analysis, and the Youden Index = 11 was obtained, that is, patients with a score greater than 11 are predicted not to achieve R0 resection. In this case, patients with a score greater than 11 points clinically need to choose a neoadjuvant chemotherapy treatment option instead of initial tumor cell reduction surgery.

[0043] The present invention uses the ROC curve to compare the accuracy of the R0 resection prediction model of the present invention, the Fagotti score and the CTsuidan score. The comparison shows that among these three scoring systems, the score of the R0 resection prediction model of the embodiment of the present invention is higher than that of the other models.

[0044] An embodiment of the present invention also relates to a method for predicting R0 resection of initial tumor cell reduction surgery for ovarian cancer based on the above-mentioned model. The method is not used for diagnostic purposes. The critical score of the model is 11. The possibility of R0 resection is judged by the R0 resection prediction score. When the R0 resection prediction score is greater than 11, it is a low R0 resection possibility, and when the model score is less than or equal to 11, it is a high R0 resection possibility.

[0045] For example, if the patient's preoperative CA125 is 1000, and there is a 2-cm lesion on the splenic diaphragmatic surface and a 1-cm lesion in the hepatorenal crypt, the patient's R0 resection prediction score is 2+6+4=12 points. If the R0 resection prediction score is greater than 11 points and initial tumor cytoreductive surgery cannot achieve R0, the treatment strategy of neoadjuvant chemotherapy should be considered.

[0046] Specifically, when the R0 resection prediction score was 0-3, the R0 resection rate was 93%-96%; when the R0 resection prediction score was 4-8, the R0 resection rate was 69%-72%; when the R0 resection prediction score was 9-14, the R0 resection rate was 42%-45%; when the R0 resection prediction score was >14, the R0 resection rate was 7%-10%.

[0047] An embodiment of the present invention also relates to a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the preoperative evaluation and R0 resection prediction model of the above-mentioned initial tumor cell reduction surgery for ovarian cancer are completed.

[0048] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0049] An embodiment of the present invention also relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer when executing the program. The memory is used to store computer-readable instructions non-temporarily; the processor is used to run the computer program, and when the computer program is run by the processor, the following steps are performed: the R0 resection prediction score is calculated according to the 6 factor scores of ovarian cancer patients, including: multiplying the 6 factors by their corresponding values, and adding them to obtain the R0 resection prediction score; the 6 factors include: CA125≥560U / mL, PNI≤45.7, spleen diaphragm surface lesion score, liver and kidney crypt lesion score, small intestinal mesentery lesion score, and total score of upper abdominal lesions. The gene expression levels in the samples described above for constructing the risk score model are used to calculate the risk score using the risk score model described above.

[0050] Embodiment 1:

[0051] 1. Model construction

[0052] 1.1 Research subjects: A total of 200 patients with advanced epithelial ovarian cancer who underwent tumor cell reduction surgery after evaluation with the preoperative imaging evaluation system for ovarian cancer in the Department of Obstetrics and Gynecology of the First Affiliated Hospital of Air Force Medical University from January 2019 to June 2023 were collected for model training.

[0053] 1.2 Inclusion and Exclusion Criteria

[0054] Inclusion criteria:

[0055] (1) The postoperative resection specimen was confirmed as epithelial ovarian cancer (including primary fallopian tube cancer and peritoneal cancer) by the pathology department of Xijing Hospital;

[0056] (2) Age 18–80 years old;

[0057] (3)FIGO stage IIIA-IV.

[0058] Exclusion criteria:

[0059] (1) Recurrent ovarian cancer or patients with multiple surgeries;

[0060] (2) Combined with other primary malignant tumors or a history of other malignant tumors;

[0061] (3) patients with poor physical condition whose ECOG score was more than 3 points;

[0062] (4) Patients with incomplete information or who refused to join.

[0063] 1.3 Clinical information collection

[0064] Patient clinical data:

[0065] (1) General preoperative information: age, gravidity, parity, family history, smoking history, previous abdominal surgery history, preoperative hypertension, preoperative diabetes, preoperative heart disease, BMI, and whether neoadjuvant chemotherapy was used;

[0066] (2) Preoperative serological indicators: hemoglobin, platelets, serological albumin, PNI, serological cancer antigen 125 (CA125), serological human epididymis protein 4 (HE4), and vascular endothelial growth factor (VEGF) within 3 days before surgery;

[0067] (3) Intraoperative and postoperative information: scope of surgery, operation time, type and grade of postoperative complications, treatment of complications, recovery time of complications, whether the patient was admitted to the intensive care unit after surgery, and whether the patient died due to complications.

[0068] Construction of a comprehensive evaluation system for ovarian cancer: Establishment of a preoperative evaluation system based on imaging examinations.

[0069] The preoperative evaluation system included 29 anatomical sites and the presence of ascites. Lesions in the same sites were directly measured during surgery to obtain intraoperative evaluation data as the gold standard and outcome events, as shown in Table 2.

[0070] Table 2

[0071]

[0072]

[0073] 1.4 Patient groups

[0074] Patients were divided into the optimal cytoreductive surgery group (R0 group) and the suboptimal cytoreductive surgery group (non-R0 group) according to the surgical results. Patients in the R0 group meant that during the operation, all tumor tissues visible to the naked eye were completely removed, and there were no residual tumor cells. In the non-R0 group, the tumor could not be completely removed due to various factors such as the location, size, invasion range, and specific conditions of the patient. The preoperative evaluation results are shown in Table 3.

[0075] Table 3: Preoperative evaluation results

[0076]

[0077] According to the data in Table 3, the preoperative MRI evaluation results showed that there was a statistically significant difference between the R0 group and the non-R0 group (P<0.001). The preoperative CT evaluation results also showed a statistically significant difference between the R0 group and the non-R0 group (P=0.032). And the preoperative evaluation results corresponded well with the intraoperative evaluation results, indicating that the accuracy of the preoperative evaluation model of the present invention is good.

[0078] In order to comprehensively evaluate the preoperative status of patients with ovarian cancer, clinical and imaging factors that can reflect the overall status of patients were combined for calculation. Univariate analysis showed that 3 clinical criteria and 11 imaging criteria were statistically significantly associated with non-RO resection. Specifically, 67.74% (42 / 62) of patients with CA-125 levels ≥ 560U / mL had a non-R0 surgical outcome, while only 34.18% (27 / 79) of patients with CA-125 levels < 560U / mL had a non-R0 surgical outcome (OR 4.044, 95% CI 1.995-8.201). Lesions on the splenic diaphragmatic surface and mesentery were the most valuable factors for predicting a non-R0 surgical outcome. The surgical outcome was non-R0 in 67.74% (42 / 62) of patients with splenic diaphragmatic surface, while only 25.32% (20 / 79) of patients without such findings had a non-R0 surgical outcome (OR 2.596, 95% CI 1.6614-4.176).

[0079] In order to describe the overall situation of intra-abdominal tumor burden in more detail, the abdomen was divided into three regions: upper, middle, and lower. The OR value of the upper abdomen was 1.278 (95% CI 1.149-1.421). After forward feature selection of multivariate analysis, 2 clinical factors and three imaging factors were still associated with incomplete cytoreduction and were statistically significant, as shown in Table 4.

[0080] Table 4: Multivariate regression analysis based on clinical features and preoperative imaging features to predict residual lesions after ovarian cancer surgery

[0081]

[0082]

[0083] Clinical factors were CA-125 level ≥560U / mL (OR 2.925, 95% CI 1.147-7.646) and PNI ≤45.7 (OR 2.898, 95% CI 1.090-7.707), and imaging factors were lesions on the diaphragmatic surface of the spleen (OR 3.970, 95% CI 1.528-9.962), lesions in the hepatorenal crypts (OR 2.053, 95% CI 1.140-3.696), mesenteric lesions (OR 2.468, 95% CI 1.132-5.380), and upper abdominal burden (OR 1.278, 95% CI 1.149-1.421). All OR values ​​were statistically significant, p<0.05.

[0084] Construction of a prediction model for ovarian cancer surgical outcomes: The assignment method is shown in Table 5.

[0085] Table 5: Prediction scores for ovarian cancer surgery outcomes

[0086] factor Assignment CA125≥560 2 PNI≤45.7 2 Spleen diaphragm surface (lesion score = 0, 1, 2, 3) × 3 Hepatorenal crypts (lesion score = 0, 1, 2, 3) × 2 Small Intestinal Mesentery (lesion score = 0, 1, 2, 3) × 2 Total score for upper abdominal lesions Total score for upper abdominal lesions <![CDATA[Total * >

[0087] *The prediction score for ovarian cancer surgery outcome is obtained by adding the prediction scores of the six factors.

[0088] Through the analysis of the patient cohort, the R0 resection rates of patients with prediction scores of 0-3, 4-8, 9-14, and >14 were 94.87%, 70.27%, 43.33%, and 8.57%, respectively. The median (range) of the prediction score was 8 (0-44), and the area under the ROC curve (AUC) for predicting R0 resection was 0.8727.

[0089] ROC curve analysis was used to evaluate the accuracy of suboptimal cytoreduction prediction scores, Fagotti score, and Suidan score. Figure 1 As shown (the vertical axis 80% from left to right, the curves are the model score of the present invention, Suidan score, and Fagotti score, respectively). In the data set of this experiment, the AUC of the Fagotti score was 0.6560, while the Suidan score obtained an AUC of 0.8308. Among the three scoring systems, the model MRI score of the embodiment of the present invention showed the highest AUC, which was 0.8727.

[0090] Example 2

[0091] Forty patients with advanced ovarian cancer from the Department of Obstetrics and Gynecology, Second Affiliated Hospital of Air Force Medical University from June 2023 to April 2024 were selected for external validation of the model. The inclusion and exclusion criteria were the same as those for the model construction cohort.

[0092] The experimental results are as follows Figure 2 As shown in the figure, the AUC of the model on the external validation dataset is 0.8151, which means that the model has a high classification performance.

[0093] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments described herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer, characterized in that: include: Data acquisition module: used to obtain tumor diameter data of anatomical sites related to ovarian cancer and whether there is ascites; Preoperative evaluation module: used to calculate the tumor diameter data according to the tumor lesion size to obtain the lesion score; R0 resection prediction module: used to calculate the R0 resection prediction score, including: multiplying the six factors by their corresponding values ​​respectively, and adding them to obtain the R0 resection prediction score; The six factors include: CA125 ≥ 560 U / mL, PNI ≤ 45.7, spleen diaphragm surface lesion score, liver and kidney crypt lesion score, small intestinal mesentery lesion score, and total upper abdominal lesion score.

2. The preoperative evaluation and R0 resection prediction model according to claim 1, characterized in that: The anatomical sites related to ovarian cancer include: liver diaphragm, spleen diaphragm, central tendon, liver capsule, liver hilum, liver ligament, gallbladder fossa, hepatorenal recess, liver parenchyma, stomach surface, lesser omentum, spleen capsule, spleen-stomach space, liver area, spleen area, transverse colon area, small intestinal mesentery, small intestinal surface, ileocecal part, sigmoid colon, left paracolic gutter peritoneum, right paracolic gutter peritoneum, rectum, Douglas pouch, bladder peritoneal reflection, left or right appendages, suprarenal vein lymph nodes, subrenal vein lymph nodes, left or right pelvic lymph nodes.

3. The preoperative evaluation and R0 resection prediction model according to claim 1 or 2, characterized in that: The calculation method for assigning values ​​according to the size of the tumor lesion is to calculate the lesion score based on the tumor diameter at that location, including: 0 < When the tumor diameter is < 0.5 cm, the lesion score is 1 point; 0.5≤When the tumor diameter is <5 cm, the lesion score is 2 points; Tumor diameter ≥ 5 cm or confluent lesions: lesion score 3 points; If there is ascites, the lesion score is 1 point; if there is no ascites, the lesion score is 0 point.

4. The preoperative evaluation and R0 resection prediction model according to claim 3, characterized in that: Tumor diameter data were obtained by magnetic resonance imaging.

5. The preoperative evaluation and R0 resection prediction model according to claim 1, characterized in that: When CA125 ≥ 560 U / mL, the score was assigned as 2 points; When PNI ≤ 45.7, the score was assigned as 2 points; The value of the spleen diaphragm surface was assigned as follows: the lesion score of the tumor at the spleen diaphragm surface × 3; The value assignment method for the hepatorenal crypts was as follows: the lesion score of the tumor in the hepatorenal crypts × 2; The scoring method for the small intestinal mesentery was as follows: the lesion score of the tumor in the small intestinal mesentery × 2; The total score of epigastric lesions is assigned as follows: total score of epigastric lesions × 1.

6. The preoperative evaluation and R0 resection prediction model according to claim 5, characterized in that: The total score of upper abdominal lesions is the sum of the lesion scores of the liver diaphragm surface, spleen diaphragm surface, central tendon, liver capsule, liver hilum, liver ligaments, gallbladder fossa, hepatorenal recess, liver parenchyma, stomach surface, lesser omentum, spleen capsule and spleen-stomach space obtained by the data acquisition module.

7. A method for predicting R0 resection of primary cytoreductive surgery for ovarian cancer based on the model according to any one of claims 1 to 6, wherein the method is not used for diagnostic purposes, and is characterized in that: The critical score of the model is 11, and the possibility of R0 resection is judged by the R0 resection prediction score. When the R0 resection prediction score is greater than 11, it indicates a low possibility of R0 resection, and when the model score is less than or equal to 11, it indicates a high possibility of R0 resection.

8. The method according to claim 7, characterized in that When the R0 resection prediction score was 0-3, the R0 resection rate was 93%-96%; when the R0 resection prediction score was 4-8, the R0 resection rate was 69%-72%; when the R0 resection prediction score was 9-14, the R0 resection rate was 42%-45%; when the R0 resection prediction score was >14, the R0 resection rate was 7%-10%.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer as described in any one of claims 1 to 6.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the preoperative evaluation and R0 resection prediction model for initial cytoreductive surgery for ovarian cancer according to any one of claims 1 to 6 is implemented.

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