Machine learning-based CT radiomics for identifying high risk esophageal varices of bleeding in hepatocellular carcinoma.

Abstract

Purpose: Esophageal variceal (EV) bleeding is a lethal complication in patients with hepatocellular carcinoma (HCC). Current risk stratification relies on invasive endoscopy or systemic surrogates of liver cirrhosis. This study aimed to develop a machine learning model utilizing EV-specific CT radiomics to identify high-risk esophageal varices, as defined by endoscopic criteria, in patients with HCC.

Methods: 151 HCC patients were enrolled and divided into a training cohort (n = 101; 75 high-risk; January 2017 - December 2022) and an independent temporal validation cohort (n = 50; 29 high-risk; January 2023 - December 2024). Endoscopic stigmata were used as an endpoint, including active bleeding or varices with high-risk features. Radiomic features were extracted from contrast-enhanced CT images acquired within six months prior to endoscopy. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), Brier score, calibration curves, and decision curve analysis (DCA).

Results: In the training cohort, the varix-specific radiomics signature developed via the least absolute shrinkage and selection operator (LASSO) demonstrated excellent discriminative power (cross-validated AUC: 0.956; 95% CI 0.872-1.000). In the temporal validation cohort, this model maintained high generalizability, yielding an AUC of 0.926 (95% CI 0.846-0.984, sensitivity: 93.1%, specificity: 71.4%) as well as a Brier score of 0.1186 and good calibration (intercept = -0.157, slope = 1.354). Surface-to-Volume Ratio (SVR) and NGTDM Coarseness are feasible radiomics.

Conclusion: The varix-specific radiomics model provides highly sensitive and mathematically stable non-invasive risk stratification for EV bleeding in patients with HCC.