Casper Mihl1, Daan Loeffen1, Mathijs O Versteylen2, Richard A P Takx3, Patricia J Nelemans4, Estelle C Nijssen3, Fernando Vega-Higuera5, Joachim E Wildberger1, Marco Das6. 1. Department of Radiology, Maastricht University Medical Center, P. Debyelaan 25, PO Box 5800, 6202 AZ Maastricht, The Netherlands; CARIM, School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands. 2. Department of Cardiology, Maastricht University Medical Center, Maastricht, The Netherlands. 3. Department of Radiology, Maastricht University Medical Center, P. Debyelaan 25, PO Box 5800, 6202 AZ Maastricht, The Netherlands. 4. Department of Epidemiology, University of Maastricht, Maastricht, The, Netherlands. 5. Siemens AG Healthcare Sector, Computed Tomography, 91301 Forchheim, Germany. 6. Department of Radiology, Maastricht University Medical Center, P. Debyelaan 25, PO Box 5800, 6202 AZ Maastricht, The Netherlands; CARIM, School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands. Electronic address: m.das@mumc.nl.
Abstract
BACKGROUND: Epicardial adipose tissue (EAT) is emerging as a risk factor for coronary artery disease (CAD). OBJECTIVE: The aim of this study was to determine the applicability and efficiency of automated EAT quantification. METHODS: EAT volume was assessed both manually and automatically in 157 patients undergoing coronary CT angiography. Manual assessment consisted of a short-axis-based manual measurement, whereas automated assessment on both contrast and non-contrast-enhanced data sets was achieved through novel prototype software. Duration of both quantification methods was recorded, and EAT volumes were compared with paired samples t test. Correlation of volumes was determined with intraclass correlation coefficient; agreement was tested with Bland-Altman analysis. The association between EAT and CAD was estimated with logistic regression. RESULTS: Automated quantification was significantly less time consuming than automated quantification (17 ± 2 seconds vs 280 ± 78 seconds; P < .0001). Although manual EAT volume differed significantly from automated EAT volume (75 ± 33 cm(³) vs 95 ± 45 cm(³); P < .001), a good correlation between both assessments was found (r = 0.76; P < .001). For all methods, EAT volume was positively associated with the presence of CAD. Stronger predictive value for the severity of CAD was achieved through automated quantification on both contrast-enhanced and non-contrast-enhanced data sets. CONCLUSION: Automated EAT quantification is a quick method to estimate EAT and may serve as a predictor for CAD presence and severity.
BACKGROUND: Epicardial adipose tissue (EAT) is emerging as a risk factor for coronary artery disease (CAD). OBJECTIVE: The aim of this study was to determine the applicability and efficiency of automated EAT quantification. METHODS: EAT volume was assessed both manually and automatically in 157 patients undergoing coronary CT angiography. Manual assessment consisted of a short-axis-based manual measurement, whereas automated assessment on both contrast and non-contrast-enhanced data sets was achieved through novel prototype software. Duration of both quantification methods was recorded, and EAT volumes were compared with paired samples t test. Correlation of volumes was determined with intraclass correlation coefficient; agreement was tested with Bland-Altman analysis. The association between EAT and CAD was estimated with logistic regression. RESULTS: Automated quantification was significantly less time consuming than automated quantification (17 ± 2 seconds vs 280 ± 78 seconds; P < .0001). Although manual EAT volume differed significantly from automated EAT volume (75 ± 33 cm(³) vs 95 ± 45 cm(³); P < .001), a good correlation between both assessments was found (r = 0.76; P < .001). For all methods, EAT volume was positively associated with the presence of CAD. Stronger predictive value for the severity of CAD was achieved through automated quantification on both contrast-enhanced and non-contrast-enhanced data sets. CONCLUSION: Automated EAT quantification is a quick method to estimate EAT and may serve as a predictor for CAD presence and severity.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Micaela Iantorno; Sahar Soleimanifard; Michael Schär; Todd T Brown; Gabriele Bonanno; Patricia Barditch-Crovo; Lena Mathews; Shenghan Lai; Gary Gerstenblith; Robert G Weiss; Allison G Hays Journal: Atherosclerosis Date: 2018-08-17 Impact factor: 5.162