(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively. The Coronary Artery Calcium Score (CACS) was extracted and tissue quantification was performed by a deep learning network. (3) Results: The Computed Tomography (CT) segmentations predicted by the network indicated no significant correlation between EAT volume and EAT radiodensity when compared to Agatston score (r = 0.18, r = -0.09). CACS 0 category patients showed significantly lower levels of total EAT and PAT volumes and higher EAT and PAT densities than CACS 1-99 category patients ( < 0.01). Notably, this difference did not reach significance regarding EAT attenuation in male patients. Women older than 50 years, thus more likely to be postmenopausal, were shown to be at higher risk of coronary calcification ( < 0.01, OR = 4.59). CACS 1-99 vs. CACS ≥100 category patients remained below significance level (EAT volume: = 0.087, EAT attenuation: = 0.98). (4) Conclusions: Our study proves the feasibility of a fully automated adipose tissue analysis in clinical cardiac CT and confirms in a large clinical cohort that volume and attenuation of EAT and PAT are not correlated with CACS. Broadly available deep learning based rapid and reliable tissue quantification should thus be discussed as a method to assess this biomarker as a supplementary risk predictor in cardiac CT.
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http://dx.doi.org/10.3390/jcm10020356 | DOI Listing |
Eur J Prev Cardiol
January 2025
Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, P. R. China.
Aim: To assess the relationship between body mass index (BMI), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), epicardial adipose tissue (EAT), pericardial adipose tissue (PAT) and clinical outcomes in dilated cardiomyopathy (DCM) patients.
Methods: Non-ischemic DCM patients were prospectively enrolled. Regional adipose tissue, cardiac function, and myocardial tissue characteristics were measured by cardiac magnetic resonance (CMR).
Quant Imaging Med Surg
December 2024
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background And Objective: Cardiac adipose tissue's (CAT) role in the pathogenesis of coronary artery disease (CAD) has become a hot topic in the literature in recent years. Cardiac computed tomography (CCT), a cutting-edge imaging modality, has become vital in quantifying and characterizing CAT. CAT is divided into paracardial adipose tissue (PAT), epicardial adipose tissue (EAT), and pericoronary adipose tissue (PCAT).
View Article and Find Full Text PDFJ Eat Disord
September 2024
University of California, Department of Psychiatry, University of California, San Diego, CA, USA.
Eur Radiol
September 2024
Department of Radiology, Medical University Innsbruck, Innsbruck, Austria.
Objective: Novel pericardial adipose tissue imaging biomarkers are currently under investigation for cardiovascular risk stratification. However, a specific compartment of the epicardial adipose tissue (EAT), lipomatous hypertrophy of the interatrial septum (LHIS), is included in the pericardial fat volume (PCFV) quantification software. Our aim was to evaluate LHIS by computed tomography angiography (CTA), to elaborate differences to other pericardial adipose tissue components (EAT) and paracardial adipose tissue (PAT), and to compare CT with [F]FDG-PET.
View Article and Find Full Text PDFCardiovasc Diabetol
June 2024
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Mazowiecka 15, 92-215, Lodz, Poland.
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