Predicting Progression-free Survival in Primary Liver Cancer Patients via Radiomics from DSA during TACE

Purpose: This study aimed to establish a radiomics model based on DSA during TACE to predict transcatheter arterial chemoembolization in patients with PLC who have undergone TACE treatment. Methods: A retrospective cohort of 133 TACE patients split into training (79) and validation (54) sets extracted radiomics features from DSA images, followed by consistency assessment, feature dimension reduction, and computation of the radiomics score (Radscore). Radiomics models, clinical models, and combined radiomics and clinical models were established on the basis of the Radscore and independent clinical risk factors. Goodness-of-fit assessments were performed for all the models, and calibration and decision curves were used to evaluated their calibration ability and clinical utility. Results: After applying multiple feature reduction methods, 15 radiomics features were ultimately selected to calculate the Radscore. The mPFS in the low-risk group was significantly longer than that in the high-risk group (training: 15.7 vs. 5.5 months, P < 0.001; validation: 9.3 vs. 2.4 months, P = 0.0012). The combined model outperformed both radiomics and clinical models with higher AUCs, better AIC and BIC values, and a higher log-likelihood. Calibration curves and decision curves confirmed its superior predictive accuracy and clinical utility. Conclusion: The Radscore based on DSA radiomics features can be used to stratify patients into risk groups, and nomograms based on intraoperative DSA radiomics and clinical indicators during TACE constitute a novel strategy to predict PFS in PLC patients receiving TACE treatment.