Stratification of children with myocarditis using radiomics signatures in LGE cardiovascular MRI

Pediatric patients with myocarditis present with heterogeneous symptoms, disease courses and outcomes. Late Gadolinium Enhancement (LGE) in cardiovascular magnetic resonance imaging (CMR) is a routine diagnostic tool, but relationships between LGE patterns and patient characteristics are typically assessed qualitatively. We investigate the use of radiomic features to quantify LGE texture and location to identify signatures that stratify pediatric myocarditis cases.

We compared radiomic features in a digital phantom across different resampling strategies to address variability in patient size and imaging parameters. Non-negative matrix factorization (NMF) was applied to spatially resolved radiomic features of the left myocardium to identify distinct radiomic signatures in a pediatric cohort with confirmed myocarditis. Clinical parameters were compared across the resulting groups, and correlations between image meta-features and outcomes explored. A user-friendly software tool offers feature extraction and signature calculation on unseen data and comparison of new patients to the existing cohort.

The phantom experiments showed improved comparability of radiomic features when resampled to uniform voxel density (voxel count per myocardial diameter) rather than uniform voxel size. After appropriate pre-processing, NMF identified four patient groups with distinct LGE signatures within 195 patients (median age 16 years, 19% female). One group separates out patients with signs of heart failure, correlating with left-ventricular ejection fraction (r=-0.38, 95% CI [-0.50,-0.25]) and log(NT-proBNP) (r=0.36,[0.21,0.50]). A second group's dominant meta-feature correlates with myocardial edema (r=0.27,[0.13,0.40]) and ventricular tachycardia (r=0.19,[0.05,0.32]); a third indicates mild presentation. The clinical relevance of the fourth remains unclear.

Spatially resolved radiomic features from suitably resampled LGE CMR images yield quantitative LGE signatures associated with clinical characteristics in pediatric myocarditis, supporting improved stratification and personalized management in the long run.