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

Purpose: 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. Patients and Methods: 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. Results: 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. Conclusion: 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. Keywords: radiomics, prognosis, prognostic model, risk stratification, convolutional neural network, immunotherapy, tyrosine kinase inhibitor, locoregional therapy, survival analysis