Clinically deployable AI to predict objective response to radiotherapy-intensified immunotherapy in advanced hepatocellular carcinoma

Background This study aimed to evaluate whether radiotherapy enhances outcomes in advanced hepatocellular carcinoma (HCC) treated with immune–targeted therapy and to develop an interpretable artificial intelligence model for predicting response. Methods In this multicenter retrospective study, 238 patients with HCC receiving immune–targeted therapy across three hospitals were included and categorized into an RT group or a no-radiotherapy (No-RT) group according to whether RT was delivered during treatment. Propensity score matching (PSM) was applied to mitigate baseline imbalance. For objective response rate (ORR) prediction, patients were randomly split (7:3) into training and validation cohorts, and eight AI models were developed and evaluated. Results ORR was higher in the RT group than in the No-RT group (43.3% vs 28.8%, P = 0.02). RT was associated with longer overall survival (OS) and progression-free survival (PFS) both before and after PSM (all P < 0.05). Responders exhibited markedly improved OS and PFS compared with non-responders (both P < 0.001). Among eight models, the multilayer perceptron (MLP) achieved the best discrimination in the validation cohort (AUC-ROC = 0.71). SHapley Additive exPlanations (SHAP) highlighted age, tumor size, alpha-fetoprotein (AFP), and RT status as the dominant contributors. Conclusions In advanced HCC, adding RT to immune–targeted therapy was associated with improved response and survival. An interpretable MLP model may offer a feasible, clinic-friendly approach to ORR prediction and support individualized immunoradiotherapy decisions.