ArticleFrontiers in cardiovascular medicine2021
Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
- Cardiac Magnetic Resonance Radiomics for Diagnosis, Phenotyping and Risk Stratification of Cardiomyopathies.Healthcare (Basel, Switzerland) · 2026Review
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Can multiparametric FDG-PET/MRI analysis really enhance the prediction of myocardial recovery after CTO revascularization? A machine learning study.Zeitschrift fur medizinische Physik · 2026Article
- Patient-level CAD-RADS scoring from coronary radiomic features.Scientific reports · 2026Article
- Epigenetic regulation of cardiac physiology and pathophysiology: biological sex matters.The journal of cardiovascular aging · 2026Article
- Left Ventricular Non-Compaction Cardiomyopathy: A Review of the Pathophysiology, Epidemiology, Diagnosis, Genetics, and Clinical Management.Journal of personalized medicine · 2025Review
- Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes.European heart journal. Digital health · 2025Review
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.Journal of clinical medicine · 2025Review
- Development and validation of an ECG-based nomogram for early diagnosis of dilated cardiomyopathy.American journal of translational research · 2025Article
- Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.Cardiovascular diagnosis and therapy · 2024Review
- A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.Journal of imaging informatics in medicine · 2024Article
- Watchful Waiting: Echocardiographic Surveillance of Childhood Left Ventricular Noncompaction.JACC. Advances · 2024Article
- Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.Diagnostics (Basel, Switzerland) · 2024Review
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes.Radiology · 2023Article
- A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography.Frontiers in radiology · 2023Article
- Atrial fibrillation prediction by combining ECG markers and CMR radiomics.Scientific reports · 2022Article
- Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Review
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Left Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction-LVEF-), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine-SVM-, Logistic Regression-LR-, and Random Forest Classifier-RF-) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.
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