Development and validation of a machine learning-based clinical prediction model for monitoring liver injury in patients with pan-cancer receiving immunotherapy

作者：Yi Wang, Jing Lei, Zhiping Jin, Ying Jiang, Ning‐Ping Zhang, Minzhi Lv, Tianshu Liu · 发表于：International Journal of Medical Informatics · 年份：2025 · DOI：10.1016/j.ijmedinf.2025.106036 · 被引用次数：9 · 研究领域：Cancer Immunotherapy and Biomarkers、Hepatocellular Carcinoma Treatment and Prognosis、Ferroptosis and cancer prognosis

BACKGROUND: Immune checkpoint inhibitor (ICI)-related liver injury poses a considerable clinical challenge for cancer patients. This study aimed to develop and validate an interpretable predictive model employing machine learning (ML) algorithms to accurately identify patients at high risk of acute liver injury within one month of initiating ICI treatment. METHODS: This longitudinal cohort study included pan-cancer patients who received their first ICI treatment between March 2019 and September 2022 at Zhongshan Hospital. Six ML algorithms, namely neural networks (NN), gradient boosting classifier (GBC), eXtreme gradient boosting (XGBoost), logistic regression (LR), categorical boosting classifier (CatBoost) and random forest (RF), were utilized to construct predictive models for acute ICI-related liver injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and brier score (BS). The SHapley Additive exPlanations (SHAP) method was applied to rank the feature importance and interpret the final model, providing insights into the contribution of each feature to liver injury prediction, thereby enhancing clinical interpretability. This study is registered with the Chinese Clinical Trial Registry (ChiCTR2300067470). RESULTS: A total of 863 patients were enrolled in the study, with 22.71% experiencing liver injury within one mo...