Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.

Abstract

Heart failure remains a major source of global morbidity and mortality, frequently driven by the structural and functional myocardial changes associated with ischemic and non-ischemic cardiomyopathies. While cardiovascular magnetic resonance (CMR) is the gold standard for non-invasive ventricular assessment, standard clinical measures rely on visual human interpretation. By contrast, radiomic analysis, a high-throughput computational approach that can extract quantitative features beyond the limits of visual perception, has gained interest in its application to CMR for detailed evaluation of myocardial properties. Over the last decade, novel studies integrating radiomics with machine learning (ML) algorithms may enable more accurate diagnosis and personalized characterization of non-ischemic cardiomyopathy beyond traditional CMR sequences, and without the use of gadolinium-based contrast agents. This review provides an overview of CMR radiomic analysis, summarizes recent applications of ML workflows in non-ischemic cardiomyopathy, and discusses the challenges and opportunities in integrating these computational tools into clinical practice.