Fully Automated CT-Based Quantitative Body Composition Analysis for Predicting Survival in Patients with HCC Undergoing TACE: A Dual-Cohort Study

Transarterial chemoembolization (TACE) is a standard treatment for patients with unresectable hepatocellular carcinoma (HCC), yet existing models provide limited individualized risk stratification. Automated CT-derived body composition analysis has emerged as an objective marker of patient physiological reserve, but its value in prognostication in TACE patients is insufficiently studied. Therefore, the aim of the study was to evaluate the prognostic value of a fully automated, open-source pipeline for CT-based body composition analysis in predicting overall survival (OS) in patients with HCC undergoing TACE. In this study, we used two independent cohorts of treatment-naive patients undergoing TACE: the WAW-TACE cohort (development) (n = 230) and the HCC-TACE-Seg cohort (validation) (n = 100). Skeletal muscle and fat metrics were extracted from pre-treatment CTs using a standardized deep learning pipeline and normalized by sex. Survival analyses were performed using Cox proportional hazards (CoxPH) models and random survival forests (RSF). Skeletal muscle density (SMD) at the L3 level was the strongest and independent predictor of OS across both cohorts (HR: development, 0.84; p = 0.029; validation, 0.79; p = 0.028). This association remained significant after adjustment for the best-performing clinical composite scores: mHAP-2 in the development (adjusted HR = 0.68; p = 0.049) and CLIP in the validation cohort (adjusted HR = 0.43; p = 0.003). In CoxPH, the addition of SMD metrics resulted in only modest improvements in discrimination (ΔC-index 0.011–0.037) that did not reach statistical significance. In contrast, RSF analysis demonstrated a statistically significant improvement in model discrimination when muscle-based variables were added to clinical features (ΔC-index = 0.023; p < 0.001). Fully automated CT-derived body composition metrics were independently associated with OS in patients treated with TACE. While their addition to traditional clinical models resulted in only modest improvements in discrimination, muscle density provided complementary prognostic information. Notably, statistically significant increases in model performance were observed when muscle-based features were integrated into RSF, highlighting their potential value within machine learning frameworks. Table of Contents