@article{QIMS155064,
author = {Ailian Shen and Jing Xu and Xiuzheng Yue and Xiao Yu and Lianming Wu and Dan Mu},
title = {Non-invasive diagnostic model for myocarditis using cardiac magnetic resonance radiomics},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {16},
number = {7},
year = {2026},
keywords = {},
abstract = {Background: Myocarditis is an inflammatory myocardial disease with high clinical heterogeneity, and its early diagnosis relies on invasive endomyocardial biopsy (EMB) or advanced cardiac magnetic resonance (CMR) techniques with poor accessibility in primary hospitals. Conventional CMR interpretation is subjective with inter-observer variability, and single-modality radiomics models have insufficient diagnostic accuracy. This study aimed to construct and validate a machine learning model integrating multimodal CMR radiomics features and clinical parameters for early non-invasive and accurate diagnosis of myocarditis.Methods: This study recruited 344 participants from two hospitals (185 with myocarditis and 159 without myocarditis). Radiomics features were extracted from the left ventricular myocardium using multimodal CMR sequences [cine sequence (CINE), T2-weighted imaging (T2WI), and late gadolinium enhancement imaging (LGE)] and combined with clinical parameters. Feature selection was performed sequentially: first, intraclass correlation coefficient (ICC) analysis was used to retain features with ICC >0.8 for reproducibility; then, Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression (for dimensionality reduction). For clinical predictors, univariate logistic regression (P},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/155064}
}