Multimodal framework for TACE treatment response prediction in patients with hepatocellular carcinoma

Abstract

Transarterial chemoembolization (TACE) is a first-line treatment for intermediate-stage hepatocellular carcinoma (HCC) that can cause side effects. An accurate prediction of TACE response is important to improve clinical outcomes and avoid unnecessary toxicity. This study pursues a dual objective: to propose a standardized evaluation pipeline that enables reproducible benchmarking of state-of-the-art approaches on publicly available datasets, including both internal and external validation with public dataset, and to introduce a novel multimodal framework that integrates clinical variables, radiomic and deep features extracted from CT scans using the Vision Transformer MedViT to predict treatment response. Experiments were conducted using two publicly available datasets, the HCC-TACE-Seg, used as training and internal validation sets, and the WAW-TACE cohort, used as external validation set. The results demonstrated that the proposed method outperforms existing approaches. Independent validation on the external WAW-TACE dataset achieved promising results, confirming the robustness of the model and its potential to support treatment planning.