Interpretable CT-Based Radiomics for Prediction of High-Affinity Neoantigens Associated Immunogenic States in Advanced Hepatocellular Carcinoma Treated with TACE Plus Targeted Therapy and Immunotherapy

Objectives

To develop and validate a CT radiomics-based machine learning model for the noninvasive prediction of HANS status in advanced HCC and to investigate the prognostic and genomic characteristics associated with HANS status. Materials and Methods: This retrospective study included patients with advanced HCC treated with TACE-based triplet therapy. Patients were followed until death or last follow-up. Tumor tissue underwent whole-exome sequencing for HANS calculation, and arterial-phase CT images were used for radiomic feature extraction. A radiomics model (HANS-Rad), a clinical model (HANS-Clin), and a combined model (HANS-RC) were developed using least absolute shrinkage and selection operator regression. Model performance was evaluated using receiver operating characteristic curve analysis. Calibration and decision curve analyses were performed to assess model reliability and clinical utility. Shapley Additive Explanations (SHAP) were used for model interpretation. Genomic analyses were conducted to explore differences between predicted HANS subgroups.