Multimodal deep learning model for predicting prognosis following radiotherapy-based combination therapy in unresectable hepatocellular carcinoma.

External beam radiotherapy (EBRT)-based combination therapy yields heterogeneous survival outcomes in unresectable hepatocellular carcinoma (uHCC), underscoring the need for precise prognostic stratification. We conducted a multicenter retrospective study across six institutions, enrolling 875 uHCC patients treated with either EBRT combined with systemic therapy (ES cohort, n = 383) or EBRT combined with transarterial chemoembolization (TACE) and systemic therapy (ETS cohort, n = 492). After propensity score matching, median overall survival was significantly prolonged in the ETS cohort compared to the ES cohort (24.0 vs. 19.0 months; HR = 0.73, P < 0.0001). The multimodal deep learning model, TRIM-uHCC (transformer-based risk-stratification integrated multimodal model for uHCC), was developed to stratify patients into high-, intermediate-, and low-risk groups. Prognostic performance was compared with current guideline-based staging systems (BCLC/CNLC/AJCC-TNM) and deep learning models (Swin-Transformer/ViT/ResNet50/ResNeXt50) using the C-index and time-dependent AUC. TRIM-uHCC model showed significantly superior prognostic prediction performance compared to current guideline standards (C-indices: 0.71-0.79 vs. 0.51-0.61, all P < 0.0001) and deep learning models (C-indices: vs. 0.62-0.75, P < 0.0001-0.106) in the ETS and ES cohorts. Based on TRIM-uHCC, 8.8 % (29/331) of patients in the ES cohort could potentially achieve improved survival by adjusting to ETS, whereas 7.9 % (26/331) of patients in the ETS cohort were recommended to switch to ES treatment. Collectively, the TRIM-uHCC model offers more accurate individualized prognostic stratification than current guideline standards and other deep learning models, providing valuable decision-making support for EBRT-based combination therapies.