Analysis of the Discriminability of Three Types of CMR Image Features for Cardiomyopathy

To assess the efficacy of point cloud features, conventional image indices, and radiomics signatures from cardiovascular magnetic resonance (CMR) images, as well as their combinations in distinguishing hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) patients and normal (NOR) (healthy) subjects. A total of 452 participants (142 HCM, 157 DCM, 153 NOR) from two public datasets were analyzed. Features were extracted from the left ventricle (LV), right ventricle (RV), and myocardium (MYO) in end-diastolic (ED) and end-systolic (ES) phases, including 78 point cloud, 84 radiomics, and 20 image indices. Feature selection and SVM classification were used to create discriminative signatures. Reproducibility was assessed with a 20% training set and full test set. The combined feature model with all three types yielded the highest accuracy (92.3%, AUC 0.977), followed by point cloud and the combination of point cloud and image features (90.2% accuracy). Selected features had high repeatability (ICC ≥ 0.85), offering a detailed multi-angle view of differences among HCM, DCM, and NOR.