Cargando…
Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis
(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 quantificati...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832906/ https://www.ncbi.nlm.nih.gov/pubmed/33477874 http://dx.doi.org/10.3390/jcm10020356 |
_version_ | 1783641939717914624 |
---|---|
author | Kroll, Lennard Nassenstein, Kai Jochims, Markus Koitka, Sven Nensa, Felix |
author_facet | Kroll, Lennard Nassenstein, Kai Jochims, Markus Koitka, Sven Nensa, Felix |
author_sort | Kroll, Lennard |
collection | PubMed |
description | (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. |
format | Online Article Text |
id | pubmed-7832906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78329062021-01-26 Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis Kroll, Lennard Nassenstein, Kai Jochims, Markus Koitka, Sven Nensa, Felix J Clin Med Article (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. MDPI 2021-01-19 /pmc/articles/PMC7832906/ /pubmed/33477874 http://dx.doi.org/10.3390/jcm10020356 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kroll, Lennard Nassenstein, Kai Jochims, Markus Koitka, Sven Nensa, Felix Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title | Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title_full | Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title_fullStr | Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title_full_unstemmed | Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title_short | Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis |
title_sort | assessing the role of pericardial fat as a biomarker connected to coronary calcification—a deep learning based approach using fully automated body composition analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832906/ https://www.ncbi.nlm.nih.gov/pubmed/33477874 http://dx.doi.org/10.3390/jcm10020356 |
work_keys_str_mv | AT krolllennard assessingtheroleofpericardialfatasabiomarkerconnectedtocoronarycalcificationadeeplearningbasedapproachusingfullyautomatedbodycompositionanalysis AT nassensteinkai assessingtheroleofpericardialfatasabiomarkerconnectedtocoronarycalcificationadeeplearningbasedapproachusingfullyautomatedbodycompositionanalysis AT jochimsmarkus assessingtheroleofpericardialfatasabiomarkerconnectedtocoronarycalcificationadeeplearningbasedapproachusingfullyautomatedbodycompositionanalysis AT koitkasven assessingtheroleofpericardialfatasabiomarkerconnectedtocoronarycalcificationadeeplearningbasedapproachusingfullyautomatedbodycompositionanalysis AT nensafelix assessingtheroleofpericardialfatasabiomarkerconnectedtocoronarycalcificationadeeplearningbasedapproachusingfullyautomatedbodycompositionanalysis |