HEPANEXUS: a multimodal graph attention network-based topological embedding integrating radiomic textures and clinical biomarkers layers in hepatocellular carcinoma prognosis
Year: 2026
Type: article
Abstract: Hepatocellular Carcinoma (HCC) is one of the leading causes of mortality due to cancer globally. Transarterial Chemoembolization (TACE) is a standard intervention for intermediate-stage patients which hinders the outcome estimation due to significant inter-patient variability. Present models often process features independently, overlooking topological and relational correlations between medical images and clinical records. This research presents HepaNexus, a customized Multi-Task Graph Attention Network developed to establish an integrated framework for predicting recovery and therapeutic response. We employed an aggregate of 128 contrast-enhanced CT scans, extracting patient feature vectors and standardized clinical biomarkers modelled as nodes in a topological embedding. This architecture predicts Recovery Rate, binary TACE response, and mRECIST-based Degree of Response using a shared feature backbone and weighted multi-task loss simultaneously. HepaNexus achieved an MAE of 0.0909 for recovery prediction, corresponding to an 80% relative error reduction when compared with standard MLP baselines. In the classification tasks, the model achieved accuracies of 97.44% and 97.44% for TACE response and response degree, respectively, with a Macro F1-score of 0.8828. By transitioning from tabular instances to graph nodes, HepaNexus leverages inter-patient dependencies to encode nuanced clinical phenotypes, providing a more robust and prognostic approach to personalized prognostic assessment.
Source: Scientific Reports
Institution: Easwari Engineering College
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7203636873
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