Relationship between coronary artery disease and epicardial adipose tissue quantification at cardiac CT: comparison between automatic volumetric measurement and manual bidimensional estimation.
OBJECTIVE
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).


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.
Bastarrika
Gorka
G
Department of Radiology and Radiological Science, Ashley River Tower, 25 Courtenay Drive, Medical University of South Carolina, Charleston, SC 29401, USA.
Broncano
Jordi
J
Schoepf
U Joseph
UJ
Schwarz
Florian
F
Lee
Yeong Shyan
YS
Abro
Joseph A
JA
Costello
Philip
P
Zwerner
Peter L
PL
D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000328:Adult; D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D014057:Tomography, X-Ray Computed / Q000379:methods*