Combined Model as a Predictor of Hepatocellular Carcinoma: A Pilot Study Comparing Liver Segmental Volume Ratio and Liver Vein to Cava Attenuation

Background/Objectives: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality and often develops in the context of liver cirrhosis (LC). Early detection remains a clinical challenge, particularly with limited sensitivity of current se-rum biomarkers and qualitative imaging tools. This pilot study aimed to evaluate the diagnostic utility of two computed tomography (CT)-derived metrics, Liver Segmental Volume Ratio (LSVR) and Liver Vein to Cava Attenuation (LVCA), as non-invasive im-aging predictors of HCC in cirrhotic patients. Methods: In this observational retrospective pilot study, 36 patients with LC, with or without HCC, were enrolled from a single tertiary care center between 2021 and 2024. Demographic, clinical, biochemical, and imaging data were collected. LSVR and LVCA were calculated from contrast-enhanced CT scans. Predictors of HCC were assessed using conditional inference trees and multivariate logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). P-value < 0.05 was considered statistically significant Results: LVCA and LSVR levels were significantly higher in the HCC group (p< 0.001). In multivariate analysis, LVCA was significantly associated with HCC onset (Odds Ratio=2.88, p=0.0075). The final model incorporating both LVCA and LSVR achieved excellent discrimination (AUC=0.967), with 91% sensitivity and 88% specificity. The combined model outperformed LSVR alone (p=0.030), though not LVCA alone. Conclusions: LVCA and LSVR represent promising quantitative imaging biomarkers for non-invasive HCC detection in cirrhotic patients. External validation in multicenter co-horts and longitudinal studies assessing the temporal evolution of LSVR and LVCA are necessary to confirm their application in clinical practice.