Radiomics Using CT Images for Preoperative Prediction of Tumor Response in Hepatocellular Carcinoma Treated with...

The optimal assessment of tumor response in hepatocellular carcinoma (HCC) after drug-eluting bead transarterial chemoembolization (DEB-TACE) remains unclear. This study aimed to develop a CT-based radiomics model for the preoperative prediction of tumor response to DEB-TACE in patients with HCC.

Patients with HCC who received DEB-TACE as initial treatment from two centers were included and divided into training, internal validation and external validation cohorts. LASSO and logistic regression were used to identify the optimal radiomics features and independent predictors of tumor response, respectively, Then, a combination model was developed and assessed.

335 patients were included in this study. Radscore was calculated based on 15 identified optimal radiomics features, and maximum tumor diameter and tumor capsule were independent predictors of tumor response. A nomogram was generated based on the clinical predictors and Radscore. In the training cohort, the AUC of nomogram was significantly superior to that of both clinical (0.915 vs 0.800, P=0.004) and the radiomics models (0.915 vs 0.842, P=0.010). Calibration curves and decision curve analysis (DCA) demonstrated good consistency between the nomogram predictions and actual outcomes as well as clinical net benefit across all cohorts. The prognostic stratification based on the nomogram effectively predicted patients with potential objective response of tumor and survival.

This radiomics model had an excellent performance to predict tumor response in HCC after DEB-TACE, which may serve as a reliable tool to assist with the selection of patients for DEB-TACE.