ArticleEuropean radiology2023
Identification of fibrosis in hypertrophic cardiomyopathy: a radiomic study on cardiac magnetic resonance cine imaging.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 26 citations in OpenAlex.
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.The international journal of cardiovascular imaging · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Decoding the Heart Through Computed Tomography: Early Cardiomyopathy Detection Using Ensemble-Based Segmentation and Radiomics.Journal of imaging · 2026Article
- Integrating preoperative multiregion radiomic features with clinical data to predict atrial fibrillation recurrence after radiofrequency ablation.Frontiers in cardiovascular medicine · 2026Article
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI.Scientific reports · 2025Article
- Radiomics for differential diagnosis of ischemic and dilated cardiomyopathy using non-contrast-enhanced cine cardiac magnetic resonance imaging.La Radiologia medica · 2025Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Article
- Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.Cardiovascular diagnosis and therapy · 2024Review
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- [Clincial Research Progress in Using Magnetic Resonance Imaging to Assess Myocardial Fibrosis in Hypertrophic Cardiomyopathy].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- Next-Generation Cardiac Magnetic Resonance Imaging Techniques for Characterization of Myocardial Disease.Current treatment options in cardiovascular medicine · 2024Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.BMC medical imaging · 2024Article
- Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure.Radiology. Cardiothoracic imaging · 2024Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesHypertrophic cardiomyopathy (HCM) often requires repeated enhanced cardiac magnetic resonance (CMR) imaging to detect fibrosis. We aimed to develop a practical model based on cine imaging to help identify patients with high risk of fibrosis and screen out patients without fibrosis to avoid unnecessary injection of contrast.
methodsA total of 273 patients with HCM were divided into training and test sets at a ratio of 7:3. Logistic regression analysis was used to find predictive image features to construct CMR model. Radiomic features were derived from the maximal wall thickness (MWT) slice and entire left ventricular (LV) myocardium. Extreme gradient boosting was used to build radiomic models. Integrated models were established by fusing image features and radiomic models. The model performance was validated in the test set and assessed by ROC and calibration curve and decision curve analysis (DCA).
resultsWe established five prediction models, including CMR, R1 (based on the MWT slice), R2 (based on the entire LV myocardium), and two integrated models (I
conclusionsA predictive model that fused image and radiomic features from the entire LV myocardium had good diagnostic performance, robustness, and clinical utility. KEY POINTS: • Hypertrophic cardiomyopathy is prone to fibrosis, requiring patients to undergo repeated enhanced cardiac magnetic resonance imaging to detect fibrosis over their lifetime follow-up. • A predictive model based on the entire left ventricular myocardium outperformed a model based on a slice of the maximal wall thickness. • A predictive model that fused image and radiomic features from the entire left ventricular myocardium had excellent diagnostic performance, robustness, and clinical utility.
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Registered trials
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