ArticleOrphanet journal of rare diseases2025
Differentiation of light chain cardiac amyloidosis and hypertrophic cardiomyopathy by ensemble machine learning-based radiomic analysis of cardiac magnetic resonance.
Article in Orphanet journal of rare diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cardiac Magnetic Resonance Radiomics for Diagnosis, Phenotyping and Risk Stratification of Cardiomyopathies.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
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11 authors.
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Abstract
backgroundWe aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomyopathy (HCM).
methodsIn the development dataset, we retrospectively collected at Peking Union Medical College Hospital between January 1, 2017, and December 31, 2022, and included 84 patients with AL-CA, 63 patients with HCM, and 34 healthy controls. Radiomics features were extracted from regions of interest in the myocardium on native T1, post-contrast T1, extracellular volume (ECV), and T2 mapping. For each modal data, eight feature selection methods were used to select the top 10 important features; then, seven ML classifiers were trained with the selected features for disease classification, and the best combinations of feature selection and classifiers were chosen by the highest predictive accuracy (ACC). The predictive results of multiple ML classifiers as input to build an ensemble ML model that classified each case (AL-CA, HCM, or controls) using a"soft voting" scheme.
resultsFor native T1, post-contrast T1, T2 mapping, ECV, and clinical data, the best combination of feature selection and classifier is MRMR_RF, XGboost_RF, Lasso_ Lasso, Lasso_RF, and ANOVA_ XGboost, respectively. The myocardial texture and the first-order features of native T1, post-contrast T1, and T2 mapping dominated the ensemble ML model and there was only a marginal role for ventricular shape features. In the hold-out testing dataset (37 AL-CA, 21 HCM, and 14 controls), the ensemble ML model exhibited a better diagnostic value with an area under curve of 0.98 for differentiating the 3 groups.
conclusionAn ensemble ML model with competitive diagnostic accuracy was proposed to differentiate AL-CA from HCM patients and healthy controls.
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