HepaKineticNet: Deep Learning of CT-Derived Intratumoral Heterogeneity for Overall Survival Prediction in BCLC Stage B-C Hepatocellular Carcinoma Treated with Locoregional Therapy Plus First-Line TKI and Anti-PD-1 Therapy.

Authors

Jinpeng Li, Wenzhe Bu, Xiaotian Hanming, Qianqian Zhang, Jinlong Song, Wei Chen

Published in

Journal of hepatocellular carcinoma. Volume 13. Pages 636207. Epub Oct 01, 2026.

Abstract

To develop HepaKineticNet, a deep learning framework that models inter-phase kinetic dynamics on multi-phase contrast-enhanced CT (CECT) to generate intratumoral heterogeneity (ITH) maps, and to assess in an independent external cohort whether a clinical-radiomics fusion model improves overall survival (OS) prediction in BCLC stage B-C hepatocellular carcinoma (HCC) treated with locoregional therapy plus first-line TKI and anti-PD-1 agents.
In this bi-institutional retrospective study, HepaKineticNet was developed using 359 patients from one institution (231 training, 128 validation) and evaluated in an independent external test cohort of 95 patients from a second institution. HepaKineticNet integrates a Spatial Kinetic Differential Attention module into an nnU-Net backbone, separately encoding arterial wash-in and delayed wash-out kinetics, with multiscale coefficient-of-variation self-supervision. Ten radiomics features (hierarchical filtering) and three clinical variables selected by LASSO-penalised Cox regression (10-fold cross-validation) were used to build clinical, radiomics, and fusion Cox models.
A total of 277 deaths occurred, 128 of them in the development cohort. Baseline characteristics were balanced (all P>0.05). In the external test cohort, the fusion model achieved a C-index of 0.713, exceeding the clinical model (0.651; P=0.012). Time-dependent AUCs at 1, 2, and 3 years were 0.760 (95%CI 0.651-0.864), 0.838 (95%CI 0.736-0.930), and 0.909 (95%CI 0.686-1.000), whereas those of the clinical model were 0.676, 0.742, and 0.772. Kaplan-Meier analysis showed median OS of 9.5 vs 19.0 months for high- vs low-risk strata (HR 3.02, 95%CI 1.86-4.91; P<0.001). Discrimination was consistent across ten prespecified subgroups.
HepaKineticNet-derived ITH maps encode kinetic and spatial information that modestly improves individualized OS prediction and risk stratification in this combination-therapy HCC population, and may provide a noninvasive decision-support adjunct pending prospective validation.

PMID:
42836110
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.