Abstract 12691: Comparison of Deep Learning and Radiomic Features to Differentiate Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac MRI Cine Imaging

Abstract

Introduction: Cardiac magnetic resonance (CMR) is frequently utilized to characterize etiology of cardiomyopathy (CM), but there is need for improved disease classification, standardization in the interpretation of findings, and throughput in analysis. Radiomics has been shown to classify disease in a semi-automated manner. Alternatively, deep learning (DL) provides the ability to identify unknown features in image data. Therefore, we sought to compare DL and radiomic approaches to differentiate ischemic vs non-ischemic cardiomyopathy (ICM vs NICM), using cardiac magnetic resonance (CMR) short axis cine images. Methods: We selected 291 patients with cardiomyopathy (CM) who underwent a CMR exam at Cleveland Clinic between 2008 and 2018, of which 249 had NICM (positive label) based on expert review of the CMR exam and electronic medical record documentation. We compared a radiomic and end-to-end DL approach to identify CM etiology from short axis cine images. Automatically generated radiomic features describing myocardial shape, texture, thickness, and motion in the cine images were used to train several machine learning classifiers. In the DL approach, we directly used the cine images to train several DL classifiers, without extracting radiomic features. We evaluated the classifiers through 5-fold cross validation using the area under the curve (AUC), F1-score, and accuracy metrics. Statistical significance was evaluated using paired 2-tailed t-test at 0.05 level. Results: Support vector machine (SVM) and DenseNet121 achieved the best metrics for radiomic and DL approaches respectively. The radiomic and DL approach achieved similar AUCs of 0.852 and 0.858 respectively, but DL approach achieved statistically significant higher F1-score of 0.758 vs 0.585 of the radiomic approach. Conclusions: An end-to-end DL approach more accurately identified NICM vs ICM compared to a radiomics approach, using only cine CMR images.