Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy.

ABSTRACT:

Aims

Cardiovascular magnetic resonance (CMR) with late-gadolinium enhancement (LGE) is increasingly being used in hypertrophic cardiomyopathy (HCM) for diagnosis, risk stratification, and monitoring. However, recent data demonstrating brain gadolinium deposits have raised safety concerns. We developed and validated a machine-learning (ML) method that incorporates features extracted from cine to identify HCM patients without fibrosis in whom gadolinium can be avoided.

Methods and results

An XGBoost ML model was developed using regional wall thickness and thickening, and radiomic features of myocardial signal intensity, texture, size, and shape from cine. A CMR dataset containing 1099 HCM patients collected using 1.5T CMR scanners from different vendors and centres was used fo

SUBMITTER: Mancio J 

PROVIDER: S-EPMC9125682 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature