Objective To predict the risk factors affecting postoperative recurrence of granulomatous lobular mastitis (GLM) in the mass stage by machine learning algorithm, and to provide a reference for the early identification and prevention of postoperative recurrence of GLM in the mass stage. Methods The electronic medical records and follow-up data of patients with GLM in the Department of Breast Disease Unit, the First Affiliated Hospital of Henan University of Traditional Chinese Medicine from October 2020 to January 2023 were selected. A total of 340 patients with GLM in the mass stage who met the inclusion and exclusion criteria were selected as the research subjects. According to whether the patients relapsed after surgery, they were divided into recurrence group and non-recurrence group. The collected cases were randomly divided into training set and test set according to the ratio of 7:3. In the training set, the recurrence prediction model was constructed by using traditional logistic regression and three machine learning algorithms: artificial neural network, random forest and XGBoost (extrem gradient boosting). In the test set, the performance of the model was evaluated by sensitivity, specificity, accuracy,positive predictive value, negative predictive value, F1 value and area under the curve (AUC) value. The Shapley Additive exPlanation (SHAP) method was used to explore the important variables that affect the optimal model in identifying postoperative recurrence in the GLM mass phase. The optimal risk cutoff value of the prediction model was determined by the Youden index. Based on this, the postoperative patients in the GLM mass phase of the external test set were divided into high-risk and low-risk groups. Results A total of 392 patients who met the GLM mass stage were included, and 52 cases were excluded according to the exclusion criteria, and 340 cases were finally included, including 60 cases in the recurrence group and 280 cases in the non-recurrence group. Based on the results of univariate analysis, correlation analysis and clinically meaningful influencing factors, 12 non-zero coefficient characteristic variables were screened for the construction of the prediction model, and these 12 characteristic variables included other disease history, number of miscarriages, breastfeeding duration of the affected breast, history of milk stasis, lesion location, nipple indentation, fluctuation sensation, low-density lipoprotein, testosterone, previous antibiotic therapy, previous oral hormone medication, and perioperative traditional Chinese medicine treatment duration. The logistic regression prediction model, artificial neural network, random forest and XGBoost prediction models were constructed, and the results showed that the accuracy, positive predictive value and negative predictive value of the four prediction models were all >75%, among which the XGBoost model had the best performance, with accuracy, specificity, sensitivity, AUC, positive predictive value, negative predictive value and F1 values of 0.93, 0.99, 0.65, 0.87, 0.92, 0.93 and 0.76, respectively. SHAP method found that the duration of traditional Chinese medicine treatment during perioperative period, the duration of breast-feeding on the affected side, low density lipoprotein, testosterone and previous hormone drugs were the top five factors affecting XGBoost model to identify postoperative recurrence of GLM in mass stage. Conclusions Compared with the traditional Logistic regression prediction model, the models based on machine learning for identifying postoperative recurrence in the GLM mass phase showed better performance, among which the XGBoost model performed best. Targeted preventive measures can be given based on the above risk factors to improve the postoperative prognosis of the GLM mass phase.

Citation： XU Yueyuan, CHENG Xufeng, LIU Qi, CHENG Ziye, MENG Bingxin. Construction of a prediction model for postoperative recurrence of granulomatous mastitis in the mass stage based on machine learning. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2024, 31(12): 1482-1490. doi: 10.7507/1007-9424.202408114 Copy

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