Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning era

Figure 1 Machine learning for preoperative textbook outcome prediction in intrahepatic cholangiocarcinoma. Schematic representation of the study methodology, with details of participant selection, the data collection process, patient follow-up, development and demonstration of the machine learning model, and key findings. This study retrospectively analyzed a multicenter cohort of patients with intrahepatic cholangiocarcinoma and incorporated a comprehensive set of preoperative clinical, laboratory, and imaging variables. The machine learning model, constructed using Extreme Gradient Boosting, identified the Child-Pugh classification, Eastern Cooperative Oncology Group score, HBV status, and tumor size as the most influential predictors of textbook outcome. The application of SHapley Additive exPlanations provided enhanced interpretability, allowing for transparent risk stratification and clinical decision-making. The model demonstrated high predictive accuracy and achieved strong performance in both internal and external validation, which reinforced its potential as a valuable tool for improving surgical planning and optimizing patient outcomes in intrahepatic cholangiocarcinoma management[10]. AI: Artificial intelligence; DFS: Disease-free survival; TO: Textbook outcome; SHAP: SHapley Additive exPlanations; AUC: Area under the curve; ICC: Intrahepatic cholangiocarcinoma; XGBoost: Extreme Gradient Boosting; ML: Machine learning; ECOG: Eastern Cooperative Oncology Group.