Deep learning model for assessing survival benefits in hepatocellular carcinoma patients undergoing intra-arterial therapies based on proliferative subtype.

ABSTRACT: The proliferative hepatocellular carcinoma (HCC) subtype is associated with aggressive disease and poor prognosis, yet it remains challenging to identify non-invasively. This retrospective multicenter study developed a multitask deep learning system for detecting proliferative HCC and predicting survival after intra-arterial therapy (IAT). Contrast-enhanced CT scans from two cohorts (surgical resection, n = 398; unresectable HCC receiving transarterial chemoembolization or hepatic arterial infusion chemotherapy, n = 1749) were analyzed using an nnUNet-based segmentation pipeline for liver and tumor delineation. A novel Prototype Mamba Net (PMN) architecture was created to extract imaging features indicative of proliferative biology. The model achieved AUCs of 0.825 (95% CI: 0.781-0.884) an

SUBMITTER: Cao F 

PROVIDER: S-EPMC12630902 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature