Abstract
BACKGROUND: Budd-Chiari syndrome (BCS) is caused by obstruction of the hepatic veins or suprahepatic inferior vena cava, leading to portal hypertension and the development of gastroesophageal varices (GEVs), which are associated with an increased risk of bleeding. Existing risk models for variceal bleeding in cirrhotic patients have limited applicability to BCS due to differences in pathophysiology. Radiomics, as a noninvasive technique, holds promise as a tool for more accurate prediction of bleeding risk in BCS-related GEVs. AIM: To develop and validate a personalized risk model for predicting variceal bleeding in BCS patients with GEVs. METHODS: We retrospectively analyzed clinical data from 444 BCS patients with GEVs in two centers. Radiomic features were extracted from portal venous phase computed tomography (CT) scans. A training cohort of 334 patients was used to develop the model, with 110 patients serving as an external validation cohort. LASSO Cox regression was used to select radiomic features for constructing a radiomics score (Radscore). Univariate and multivariate Cox regression identified independent clinical predictors. A combined radiomics + clinical (R + C) model was developed using stepwise regression. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA), with external validation to evaluate generalizability. RESULTS: The Radscore comprised four hepatic and six splenic CT features, which predicted the risk of variceal bleeding. Multivariate analysis identified invasive treatment to relieve hepatic venous outflow obstruction, anticoagulant therapy, and hemoglobin levels as independent clinical predictors. The R + C model achieved C-indices of 0.906 (training) and 0.859 (validation), outperforming the radiomics and clinical models alone (AUC: training 0.936 vs 0.845 vs 0.823; validation 0.876 vs 0.712 vs 0.713). DCA showed higher clinical net benefit across the thresholds. The model stratified patients into low-, medium- and high-risk groups with significant differences in bleeding rates (P < 0.001). An online tool is available at https://bcsvh.shinyapps.io/BCS_Variceal_Bleeding_Risk_Tool/. CONCLUSION: We developed and validated a novel radiomics-based model that noninvasively and conveniently predicted risk of variceal bleeding in BCS patients with GEVs, aiding early identification and management of high-risk patients.