Tang Tianzhi, Guo Tianyu, Zhu Bo, Tian Qihui, Wu Yang, Liu Yefu
Department of Hepatobiliary and Pancreatic Surgery, Cancer Hospital of China Medical University/Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People's Republic of China.
Department of Hepatobiliary and Pancreatic Surgery, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People's Republic of China.
Sci Rep. 2025 May 3;15(1):15469. doi: 10.1038/s41598-025-97878-4.
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.
肝切除术后肝衰竭（PHLF）是肝脏手术后的一种严重并发症。我们旨在开发一种新型的、可解释的机器学习（ML）模型来预测PHLF。我们纳入了312例行肝切除术的肝细胞癌（HCC）患者，其中30%的样本用于内部验证。使用最小绝对收缩和选择算子回归结合随机森林和递归特征消除（RF-RFE）算法进行变量选择。随后，采用12种不同的ML算法来确定最佳预测模型。利用受试者工作特征曲线下面积、校准曲线和决策曲线分析（DCA）来评估模型的预测准确性。此外，对62例患者进行了独立的前瞻性验证。SHapley加法解释（SHAP）分析进一步解释了极端梯度提升（XGBoost）模型。在12个ML模型中，XGBoost模型在训练和验证队列中的AUC分别为0.983和0.981，表现出最高的准确性。校准曲线和DCA证实了该模型的准确性和临床适用性。与传统模型相比，XGBoost模型的AUC更高。前瞻性队列（AUC = 0.942）进一步证实了XGBoost模型的泛化能力。SHAP确定了三个关键变量：总胆红素（TBIL）、终末期肝病模型（MELD）评分和吲哚菁绿15分钟滞留率（ICG-R15）。此外，SHAP汇总图用于说明XGBoost对特征的正向或负向影响。XGBoost模型为可切除HCC患者的PHLF提供了良好的术前预测。
