Background: Targeted and immune therapies are used for unresectable hepatocellular carcinoma (HCC), but their efficacy is limited by tumor heterogeneity. Identifying patients who will benefit from targeted and immune therapy is crucial for optimizing treatment strategies and prognosis. We aimed to develop and validate interpretable machine learning (ML) models integrating clinical and CT imaging characteristics for pretreatment prediction of objective response and prognosis to targeted and immune therapy in HCC.

Methods: This retrospective multicenter study included 413 patients from two institutions who received targeted and immune therapy. Clinical and CT characteristics were collected. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models were developed and validated to predict treatment response and overall survival (OS). Model performance was assessed by area under the curve (AUC) and Kaplan-Meier analysis. Additionally, SHAP (SHapley Additive exPlanations) analysis explained model predictions by aggregating the attribution values for each input feature.

Results: The XGBoost model demonstrated the best performance with AUCs of 0.802 (95% CI: 0.744–0.860) and 0.805 (95% CI: 0.741–0.868) in training and validation cohorts. Significant predictors included Barcelona Clinic Liver Cancer (BCLC) stage, tumor number, tumor margin, peritumoral enhancement and macrovascular invasion. Kaplan-Meier analysis showed that high-risk scores stratified by XGBoost model were associated with shorter OS (HR: 0.740, 95% CI: 0.665–0.823, P < 0.001).

Conclusion: XGBoost model effectively predicted treatment response and prognosis in HCC patients undergoing targeted and immune therapy, offering a noninvasive tool to guide treatment decisions and optimize clinical outcomes.