A CMR-based multi-modality fusion machine learning approach for predicting left ventricular reverse remodeling in dilated cardiomyopathy

Motivation: Few multi-modality machine learning (ML) classifiers combine cardiac magnetic resonance (CMR) imaging with clinical data for predicting LVRR in DCM patients, limiting improvements in patient outcomes and management. Goal(s): To develop an ML classifier using multi-modality data, including CMR, to predict LVRR in initial DCM patients. Approach: 129 DCM patients with complete clinical and CMR data were collected. Feature selection identified relevant variables, and an LR-based nomogram was constructed and evaluated. Results: The nomogram achieved an AUC of 0.857 in the test cohort, incorporating late gadolinium enhancement pattern, global longitudinal peak strain, aldosterone antagonist, and severe mitral regurgitation. Impact: The CMR-based multi-modality nomogram has a superior ability in the prediction of LVRR in DCM patients.