CT-Based Radiomics Model for Preoperative Prediction of Histological Grade and Postoperative Survival in Hepatocellular Carcinoma

Objective To develop and validate a computed tomography (CT)-based radiomics model for predicting histological grade and postoperative survival in patients with hepatocellular carcinoma (HCC). Methods This retrospective study included 385 patients with pathologically confirmed HCC who underwent preoperative CT examinations between January 2013 and September 2022. Patients were randomly assigned to a training cohort (n = 265) and a testing cohort (n = 120). Radiomics features were extracted from portal venous phase CT images using standardized radiomics pipelines. Feature selection was performed using intraclass correlation coefficient (ICC) analysis and least absolute shrinkage and selection operator (LASSO) regression. Model performance was evaluated using receiver operating characteristic (ROC) analysis. Survival outcomes were assessed using Kaplan–Meier analysis and Cox proportional hazards regression. Results Among 396 extracted radiomics features, 180 features demonstrated good reproducibility (ICC > 0.75). After dimensionality reduction, seven radiomics features were selected to construct the radiomics signature. The model achieved an AUC of 0.889 in the training cohort and 0.941 in the testing cohort. Survival analysis identified the radiomics feature Dif.Scale1.2 as an independent predictor of overall survival (HR = 0.319, 95% CI: 0.143–0.701, P = 0.002). Conclusion CT-based radiomics may serve as a promising noninvasive imaging biomarker for predicting tumor differentiation and postoperative survival in hepatocellular carcinoma.