Epicardial adipose tissue changes after bariatric and metabolic surgery: a systematic review and meta-analysis
10.1007/s11695-023-06848-0 · doi-reference
Elucidating the primary mechanisms of high-intensity interval training for improved cardiac fitness in obesity
10.3389/fphys.2023.1170324 · doi-reference
Dynamics of intrapericardial and extrapericardial fat tissues during long-term, dietary-induced, moderate weight loss
10.3945/ajcn.117.157115 · doi-reference
The impact of exercise training plus dietary interventions on ectopic fat in population with overweight/obesity with and without chronic disease: a systematic review, meta-analysis, and metaregression of randomized clinical trials
10.1016/j.cdnut.2025.104574 · doi-reference
Tirzepatide reduces LV mass and paracardiac adipose tissue in obesity-related heart failure: SUMMIT CMR substudy
10.1016/j.jacc.2024.11.001 · doi-reference
Underlying mechanisms and cardioprotective effects of SGLT2i and GLP-1Ra: insights from cardiovascular magnetic resonance
10.1186/s12933-024-02181-7 · doi-reference
Epicardial adipose tissue GLP-1 receptor is associated with genes involved in fatty acid oxidation and white-to-brown fat differentiation: a target to modulate cardiovascular risk?
10.1016/j.ijcard.2019.04.039 · doi-reference
Human epicardial fat expresses glucagon-like peptide 1 and 2 receptors genes
10.1055/s-0043-109563 · doi-reference
Human epicardial adipose tissue expresses glucose-dependent insulinotropic polypeptide, glucagon, and glucagon-like peptide-1 receptors as potential targets of pleiotropic therapies
10.1093/eurjpc/zwad050 · doi-reference
Impact of selected glucagon-like peptide-1 receptor agonists on serum lipids, adipose tissue, and muscle metabolism-A narrative review
10.3390/ijms25158214 · doi-reference
Effect of semaglutide on epicardial adipose tissue in type 2 diabetes: insights from the STOP (semaglutide treatment effect on coronary atherosclerosis progression) randomized trial
10.1016/j.jacc.2024.05.065 · doi-reference
Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation
10.1186/s40001-023-01593-7 · doi-reference
Artificial intelligence and explanation: how, why, and when to explain black boxes
10.1016/j.ejrad.2024.111393 · doi-reference
Efficacy of cardiometabolic drugs in reduction of epicardial adipose tissue: a systematic review and meta-analysis
10.1186/s12933-023-01738-2 · doi-reference
Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers
10.1038/s43856-024-00475-1 · doi-reference
A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises
10.1109/jproc.2021.3054390 · doi-reference
Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes
10.1016/j.compbiomed.2017.02.010 · doi-reference
Automatic deep-learning segmentation of epicardial adipose tissue from low-dose chest CT and prognosis impact on COVID-19
10.3390/cells11061034 · doi-reference
Increased subcutaneous and epicardial adipose tissue production of proinflammatory cytokines in cardiac surgery patients: possible role in postoperative insulin resistance
10.1210/jc.2006-1044 · doi-reference
Human epicardial adipose tissue: a review
10.1016/j.ahj.2007.03.019 · doi-reference
Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart
10.1038/ncpcardio0319 · doi-reference
Recent progress in epicardial and pericardial adipose tissue segmentation and quantification based on deep learning: a systematic review
10.3390/app12105217 · doi-reference
Fully automatic myocardial segmentation of contrast echocardiography sequence using random forests guided by shape model
10.1109/tmi.2017.2747081 · doi-reference
Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the framingham heart study
10.1093/eurheartj/ehn573 · doi-reference
Relationship between pericoronary adipose tissue attenuation value and image reconstruction parameters
10.1016/j.heliyon.2024.e34763 · doi-reference
How do we measure epicardial adipose tissue thickness by transthoracic echocardiography?
10.5152/akd.2015.5991 · doi-reference
Reproducibility of echocardiographic measurements of epicardial fat thickness
10.1016/j.ijcard.2008.11.127 · doi-reference
Deep-learning segmentation of epicardial adipose tissue using four-chamber cardiac magnetic resonance imaging
10.3390/diagnostics12010126 · doi-reference
Quantification of epicardial fat using 3D cine dixon MRI
10.1186/s12880-020-00478-z · doi-reference
Volumetric assessment of epicardial adipose tissue with cardiovascular magnetic resonance imaging
10.1038/oby.2007.591 · doi-reference
Novel imaging biomarkers: epicardial adipose tissue evaluation
10.1259/bjr.20190770 · doi-reference
Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT
10.1259/bjr.20211274 · doi-reference
Radiomics in medical imaging-"how-to" guide and critical reflection
10.1186/s13244-020-00887-2 · doi-reference
Measurement of skeletal muscle radiation attenuation and basis of its biological variation
10.1111/apha.12224 · doi-reference
Computed tomography measured epicardial adipose tissue and psoas muscle attenuation: new biomarkers to predict major adverse cardiac events (MACE) and mortality in patients with heart disease and critically ill patients. Part I: epicardial adipose tissue
10.5114/ait.2023.130922 · doi-reference
Prognostic value of epicardial fat volume measurements by computed tomography: a systematic review of the literature
10.1007/s00330-015-3765-5 · doi-reference
Season and clinical factors influence epicardial adipose tissue attenuation measurement on computed tomography and may hamper its utilization as a risk marker
10.1016/j.atherosclerosis.2021.01.025 · doi-reference
Imaging cardiac fat
10.1093/ehjci/jet045 · doi-reference
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks
10.1007/s00330-020-07147-3 · doi-reference
Epicardial adipose tissue ceramides are related to lipoprotein lipase activity in coronary artery disease: unfolding a missing link
10.1161/atvbaha.122.317840 · doi-reference
