Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepatectomy, and 30% of the samples were utilized for internal validation. Variable selection was performed using the least absolute shrinkage and selection operator regression in conjunction with random forest and recursive feature elimination (RF-RFE) algorithms. Subsequently, 12 distinct ML algorithms were employed to identify the optimal prediction model. The area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) were utilized to assess the model's predictive accuracy. Additionally, an independent prospective validation was conducted with 62 patients. The SHapley Additive exPlanations (SHAP) analysis further explained the extreme gradient boosting (XGBoost) model. The XGBoost model exhibited the highest accuracy with AUCs of 0.983 and 0.981 in the training and validation cohorts among 12 ML models. Calibration curves and DCA confirmed the model's accuracy and clinical applicability. Compared with traditional models, the XGBoost model had a higher AUC. The prospective cohort (AUC = 0.942) further confirmed the generalization ability of the XGBoost model. SHAP identified the top three critical variables: total bilirubin (TBIL), MELD score, and ICG-R15. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects of the features as influenced by XGBoost. The XGBoost model provides a good preoperative prediction of PHLF in patients with resectable HCC.