RATIONALE AND OBJECTIVES: The aim of this study was to compare the reproducibility of bidimensional and volumetric quantification of epicardial adipose tissue (EAT) on cardiac computed tomography (CT) and evaluate their relationship with the extent of coronary artery disease (CAD). MATERIALS AND METHODS: Forty-five individuals underwent cardiac dual-source CT and conventional coronary angiography for suspicion of CAD. Nonenhanced images acquired to assess calcium score were used to quantify EAT. Coronary stenosis grading was performed on conventional coronary angiograms using Gensini scores. Two independent observers manually measured right ventricular EAT thickness at three different levels and in two different planes (four chamber and short axis) to obtain mean values. Additionally, EAT volume was automatically determined using a commercially available software tool. RESULTS: Conventional coronary angiography demonstrated nonstenotic coronary arteries in 22 subjects and significant coronary artery stenosis in 23. Significant correlations were observed between volumetric estimation of EAT and body mass index, coronary artery calcification, and Gensini score. On automatic volumetry, patients with significant coronary artery stenosis had significantly greater EAT volumes (154.58 +/- 58.91 mL) than those without significant CAD (120.94 +/- 81.85 mL) (P = .016). The manual bidimensional approach based on thickness measurements failed to show a significant difference between the two groups. Reproducibility and interobserver agreement for EAT quantification were higher when the automatic volumetric method was used (concordance-correlation coefficient, 0.96) compared to manual measurements (concordance-correlation coefficients, 0.37 for four-chamber EAT, 0.53 for short-axis EAT, and 0.58 for average EAT). CONCLUSIONS: For the quantification of EAT on cardiac CT, automated volumetry is more reproducible and correlates better with the extent of CAD than manual bidimensional measurements. Copyright (c) 2010 AUR. Published by Elsevier Inc. All rights reserved.
RATIONALE AND OBJECTIVES: The aim of this study was to compare the reproducibility of bidimensional and volumetric quantification of epicardial adipose tissue (EAT) on cardiac computed tomography (CT) and evaluate their relationship with the extent of coronary artery disease (CAD). MATERIALS AND METHODS: Forty-five individuals underwent cardiac dual-source CT and conventional coronary angiography for suspicion of CAD. Nonenhanced images acquired to assess calcium score were used to quantify EAT. Coronary stenosis grading was performed on conventional coronary angiograms using Gensini scores. Two independent observers manually measured right ventricular EAT thickness at three different levels and in two different planes (four chamber and short axis) to obtain mean values. Additionally, EAT volume was automatically determined using a commercially available software tool. RESULTS: Conventional coronary angiography demonstrated nonstenotic coronary arteries in 22 subjects and significant coronary artery stenosis in 23. Significant correlations were observed between volumetric estimation of EAT and body mass index, coronary artery calcification, and Gensini score. On automatic volumetry, patients with significant coronary artery stenosis had significantly greater EAT volumes (154.58 +/- 58.91 mL) than those without significant CAD (120.94 +/- 81.85 mL) (P = .016). The manual bidimensional approach based on thickness measurements failed to show a significant difference between the two groups. Reproducibility and interobserver agreement for EAT quantification were higher when the automatic volumetric method was used (concordance-correlation coefficient, 0.96) compared to manual measurements (concordance-correlation coefficients, 0.37 for four-chamber EAT, 0.53 for short-axis EAT, and 0.58 for average EAT). CONCLUSIONS: For the quantification of EAT on cardiac CT, automated volumetry is more reproducible and correlates better with the extent of CAD than manual bidimensional measurements. Copyright (c) 2010 AUR. Published by Elsevier Inc. All rights reserved.
Authors: Andrew H Talman; Peter J Psaltis; James D Cameron; Ian T Meredith; Sujith K Seneviratne; Dennis T L Wong Journal: Cardiovasc Diagn Ther Date: 2014-12
Authors: Jon D Klingensmith; Addison L Elliott; Amy H Givan; Zechariah D Faszold; Cory L Mahan; Adam M Doedtman; Maria Fernandez-Del-Valle Journal: J Med Imaging (Bellingham) Date: 2019-02-07
Authors: Ryo Nakazato; Ronak Rajani; Victor Y Cheng; Haim Shmilovich; Rine Nakanishi; Yuka Otaki; Heidi Gransar; Piotr J Slomka; Sean W Hayes; Louise E J Thomson; John D Friedman; Nathan D Wong; Leslee J Shaw; Matthew Budoff; Alan Rozanski; Daniel S Berman; Damini Dey Journal: Atherosclerosis Date: 2011-10-20 Impact factor: 5.162
Authors: James V Spearman; Matthias Renker; U Joseph Schoepf; Aleksander W Krazinski; Teri L Herbert; Carlo N De Cecco; Paul J Nietert; Felix G Meinel Journal: Eur Radiol Date: 2015-04-30 Impact factor: 5.315
Authors: James V Spearman; Felix G Meinel; U Joseph Schoepf; Paul Apfaltrer; Justin R Silverman; Aleksander W Krazinski; Christian Canstein; Carlo Nicola De Cecco; Philip Costello; Lucas L Geyer Journal: Eur Radiol Date: 2013-11-06 Impact factor: 5.315
Authors: Peter J Psaltis; Andrew H Talman; Kiran Munnur; James D Cameron; Brian S H Ko; Ian T Meredith; Sujith K Seneviratne; Dennis T L Wong Journal: Int J Cardiovasc Imaging Date: 2015-09-03 Impact factor: 2.357