Abstract
BACKGROUND: To investigate the preoperative factors influencing textbook outcomes (TO) in Intrahepatic cholangiocarcinoma (ICC) patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO, we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations (SHAP) technique to illustrate the prediction process. AIM: To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction. METHODS: A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China, covering the period from 2011 to 2017. Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO. Based on these variables, an EXtreme Gradient Boosting (XGBoost) machine learning prediction model was constructed using the XGBoost package. The SHAP (package: Shapviz) algorithm was employed to visualize each variable's contribution to the model's predictions. Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups. RESULTS: Among 376 patients, 287 were included in the training group and 89 in the validation group. Logistic regression identified the following preoperative variables influencing TO: Child-Pugh classification, Eastern Cooperative Oncology Group (ECOG) score, hepatitis B, and tumor size. The XGBoost prediction model demonstrated high accuracy in internal validation (AUC = 0.8825) and external validation (AUC = 0.8346). Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1, 2, and 3 years were 64.2%, 56.8%, and 43.4%, respectively. CONCLUSION: Child-Pugh classification, ECOG score, hepatitis B, and tumor size are preoperative predictors of TO. In both the training group and the validation group, the machine learning model had certain effectiveness in predicting TO before surgery. The SHAP algorithm provided intuitive visualization of the machine learning prediction process, enhancing its interpretability.