Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study

作者：Tingfeng Huang, Cong Luo, Luo-Bin Guo, Hongzhi Liu, Jiangtao Li, Qizhu Lin, Ruirui Fan, Weiping Zhou, Jingdong Li, Kecan Lin, Shi-Chuan Tang, Yongyi Zeng · 发表于：World Journal of Gastroenterology · 年份：2025 · DOI：10.3748/wjg.v31.i11.100911 · 被引用次数：14 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、Gallbladder and Bile Duct Disorders、Gastric Cancer Management and Outcomes

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...