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Article

Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

by 1,2,*, 1,3, 4, 1,2,† and 1,2,†
1
Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
2
Institute of Artificial Intelligence in Medicine, University Hospital Essen, 45147 Essen, Germany
3
Department of Radiology, Elisabeth-Krankenhaus Essen, 45138 Essen, Germany
4
Cardiologic Practice Ratingen, 40882 Ratingen, Germany
*
Author to whom correspondence should be addressed.
Contributed equally.
J. Clin. Med. 2021, 10(2), 356; https://doi.org/10.3390/jcm10020356
Received: 30 November 2020 / Revised: 14 January 2021 / Accepted: 15 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Cardiovascular Precision Medicine)
(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 (p < 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 (p < 0.01, OR = 4.59). CACS 1–99 vs. CACS ≥100 category patients remained below significance level (EAT volume: p = 0.087, EAT attenuation: p = 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. View Full-Text
Keywords: epicardial adipose tissue; paracardial adipose tissue; body composition analysis; deep learning; artificial intelligence; atherosclerosis epicardial adipose tissue; paracardial adipose tissue; body composition analysis; deep learning; artificial intelligence; atherosclerosis
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MDPI and ACS Style

Kroll, L.; Nassenstein, K.; Jochims, M.; Koitka, S.; Nensa, F. Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis. J. Clin. Med. 2021, 10, 356. https://doi.org/10.3390/jcm10020356

AMA Style

Kroll L, Nassenstein K, Jochims M, Koitka S, Nensa F. Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis. Journal of Clinical Medicine. 2021; 10(2):356. https://doi.org/10.3390/jcm10020356

Chicago/Turabian Style

Kroll, Lennard; Nassenstein, Kai; Jochims, Markus; Koitka, Sven; Nensa, Felix. 2021. "Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis" J. Clin. Med. 10, no. 2: 356. https://doi.org/10.3390/jcm10020356

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