Quantitative measurements of coronary arterial stenosis, plaque geometry, and composition are highly reproducible with a standardized coronary arterial computed tomographic approach in high-quality CT datasets.
BACKGROUND
Computed tomographic (CT) coronary angiography provides a noninvasive method for coronary plaque detection and quantification, but data are limited on reproducibility of a quantitative evaluation.


METHODS
Intrarater and interrater reliability of a semiquantitative and highly standardized, fully quantitative approach was evaluated in 480 coronary segments in 30 patients. Quantitative vessel-wall and plaque geometrical parameters (minimal lumen diameter [MLD], minimal lumen area [MLA], percentage of atheroma volume [PAV], and remodeling index [RI]) and compositional parameters (calcified plaque volume [CAP] and % of CAP [%CAP], noncalcified plaque [NCP] and % of NCP [%NCP], high-density NCP volume [HD-NCP] and % of HD-NCP [%HD-NCP] and low-density NCP volume [LD-NCP] and % of LD-NCP [%LD-NCP]) were measured. Semiquantitative agreement was evaluated by weighted κ; quantitative agreement was evaluated by concordance correlation coefficient (CCC) and Bland-Altman analysis.


RESULTS
Intraobserver agreement for MLD, MLA, and RI was excellent (CCC: 0.96, 0.96, and 0.84, respectively). Intraobserver agreement for %CAP, %HD-NCP, and %LD-NCP was also excellent (CCC: 0.99, 0.98,and 0.96, respectively). Interobserver agreement for MLD, MLA, PAV and RI was excellent (CCC: 0.98, 0.99, 0.96,and 0.86, respectively). Interobserver agreement for %CAP, % NCP, %HD-NCP, and %LD-NCP was also excellent (CCC: 0.99, 0.99, 0.98,and 0.90, respectively), and mean differences were small. Quantitative analysis showed statistically significant differences in both geometrical and compositional parameters between normal segments and those with plaque.


CONCLUSIONS
Standardized, quantitative analysis of coronary CTA datasets is reproducible for the measurement of plaque geometrical and compositional parameters and can quantify differences between normal and abnormal segments in high-quality datasets.
Piedmont Heart Institute, Atlanta, GA 30309, USA.
Sarah
S
Rinehart
Gustavo
G
Vazquez
Zhen
Z
Qian
Laura
L
Murrieta
Kim
K
Christian
Szilard
S
Voros
D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*