Prognostic value of enhanced cine cardiac MRI-based radiomics in dilated cardiomyopathy

Early precise identification of high-risk dilated cardiomyopathy (DCM) phenotype is essential for clinical decision-making and patient surveillance. The aim of the study was to assess the prognostic value of enhanced cine cardiac magnetic resonance (CMR)-based radiomics in DCM. We prospectively enrolled 401 (training set: 281; test set: 120) DCM patients. Radiomic features were extracted from enhanced cine images of entire left ventricular wall and selected by the least absolute shrinkage and selection operator. Different predictive models were built using logistic regression classifier to predict all-cause mortality and heart transplantation. Model performances were compared with the area under the receiver operating characteristic curves (AUCs). Kaplan-Meier curves, log-rank test, and Cox regression were used for survival analysis. Endpoint events occurred in 65 patients over a median follow-up period of 25.4 months. 13 radiomic features were finally selected. The Rad_Combined model integrating clinical characteristics, CMR parameters and radiomics features achieved the best performance with an AUC of 0.836 and 0.835 in the training and test sets, respectively. High-risk groups with endpoint events defined by the Rad_Combined model had significantly shorter survival time than low-risk group in both the training [Hazard Ratio (HR) = 7.74, P