Authors
Zongren Ding, Mengmeng Wu, Zheng Zeng, Guoxu Fang, Zhaodi Huang, Xinling Liu, Zhenwei Chen, Yang Zhou, Yabin Yu, Zisen Lai, Yongyi Zeng
Published in
Scientific reports. Jul 09, 2026. Epub Jul 09, 2026.
Abstract
TACE and TKI-based combination therapy shows promise for unresectable hepatocellular carcinoma (uHCC), but inter-patient heterogeneity requires reliable biomarkers for personalized management. We developed a deep learning model to predict objective response and progression-free survival (PFS) in HBV-related uHCC. We retrospectively analyzed 243 patients, partitioned into training (clinical n = 168; radiomics n = 106) and test (n = 75) datasets. Three models were constructed: a Clinical Model (C-Model), a Machine Learning Radiomics Model (ML-Model) utilizing 1,479 CT features, and a Deep Learning Model (DL-Model) based on ResNet-50. Model interpretability was addressed via Grad-CAM. Performance was evaluated using AUC and Kaplan-Meier analysis. In the test dataset, the DL-Model achieved a superior AUC of 0.851 (95% CI: 0.747-0.954), significantly outperforming the C-Model (AUC = 0.586, P < 0.05) and exceeding the ML-Model (AUC = 0.709). Survival analysis showed the DL-Model was the only framework capable of robust prognostic stratification; predicted responders had significantly prolonged PFS (P = 0.011). Grad-CAM analysis revealed a spatial dichotomy: responders exhibited focal, centralized tumor activation, whereas non-responders showed multifocal, peripheral activation patterns. The DL-Model provides a reliable, interpretable tool for predicting tumor response and PFS in uHCC patients receiving TACE and TKI-based therapy. The Grad-CAM visualization offers spatial insights into tumor heterogeneity, facilitating personalized treatment adjustments.
PMID:
42426075
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.
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