A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.

Abstract

Immune checkpoint inhibitors, particularly antibodies targeting programmed cell death 1 (PD-1), are increasingly used for advanced hepatocellular carcinoma (Ad-HCC), but treatment responses remain heterogeneous. Hyperprogressive disease (HPD) is an especially concerning pattern of rapid progression after PD-1 therapy, and reliable pre-treatment tools to identify high-risk patients are still lacking. In this multicenter retrospective study of 665 patients with Ad-HCC receiving PD-1 inhibitor-based triple therapy, we developed a transformer-based multimodal model, Hyperprogression Oncological Predictive Enhanced-model (HOPE), integrating arterial- and portal-phase computed tomography with structured clinical factors. HOPE achieved an area under the receiver operating characteristic curve of 0.801 in the internal validation cohort and 0.687 in the external validation cohort, and outperformed clinical-only and imaging-only baseline models. Ablation analyses supported the value of multimodal integration. HOPE was further supported by prespecified subgroup analyses, survival risk stratification, and Gradient-weighted Class Activation Mapping (Grad-CAM) assessments. HOPE may serve as a clinically interpretable pretreatment decision-support tool for HPD risk stratification in patients with Ad-HCC receiving PD-1 inhibitor-based triple therapy, with potential utility for closer monitoring and risk-adapted management of patients predicted to be at high risk.