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Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants ( = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants ( = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all -values. To aid interpretation, we organised the results into six feature clusters. Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.
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http://dx.doi.org/10.3389/fcvm.2021.763361 | DOI Listing |
Sci Rep
July 2025
Department of Cardiology, The Central Hospital of Shaoyang, 422000, Shaoyang, China.
Robust differentiation between infarcted and normal myocardial tissue is essential for improving diagnostic accuracy and personalizing treatment in myocardial infarction (MI). This study proposes a hybrid framework combining radiomic texture analysis with deep learning-based segmentation to enhance MI detection on non-contrast cine cardiac magnetic resonance (CMR) imaging.The approach incorporates radiomic features derived from the Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) methods into a modified U-Net segmentation network.
View Article and Find Full Text PDFBioengineering (Basel)
June 2025
Medical Innovation Research Department, Chinese PLA General Hospital, Beijing 100853, China.
Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose a dual-path hybrid deep learning framework based on CNN-LSTM and MLP, integrating anatomical features from cine CMR with biomechanical markers derived from intraventricular pressure gradients (IVPGs), significantly enhancing NICM subtype classification by capturing subtle biomechanical dysfunctions overlooked by traditional morphological models.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
July 2025
Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 160 PuJian Road, Pudong New Area, Shanghai 200127, China.
Aims: Global extracellular volume (ECV) fraction independently predicts outcomes after ST-elevation myocardial infarction (STEMI), but microvascular injuries complicate its interpretation. This study aims to assess the prognostic value of ECV-derived radiomics [radiomics score (RadScore)] from cardiac magnetic resonance (CMR) for risk stratification in reperfused STEMI patients.
Methods And Results: We retrospectively included 441 reperfused STEMI patients (mean age 60 ± 11 years; 371 males) from two centres, divided into development (n = 347) and validation cohort (n = 94) by centres.
Curr Cardiol Rep
April 2025
Department of Medicine, Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
Purpose Of Review: Pathogenetics is the study of genetics in disease pathogenesis. Many abnormal gene alleles have been identified in cardiomyopathies, but their clinical utility remains limited. This review aims to examine the integration of cardiac MRI (CMR) with genetic data to enhance early detection, prognostication, and treatment strategies for cardiomyopathies.
View Article and Find Full Text PDFEur Radiol
April 2025
Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Objectives: To evaluate the added value of the late gadolinium enhancement (LGE)-scar radiomics features in predicting reverse left ventricular remodeling (r-LVR) in ST-segment elevation myocardial infarction (STEMI) patients using machine learning (ML).
Materials And Methods: This retrospective study included 105 STEMI patients who underwent CMR within 7 days and 5 months post-percutaneous coronary intervention (PCI) on 1.5-T or 3.