Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study

• Published in: European radiology Vol. 30; no. 2; pp. 673 - 681
• Main Authors: Yu, Mengmeng, Dai, Xu, Deng, Jianhong, Lu, Zhigang, Shen, Chengxing, Zhang, Jiayin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020; Springer Nature B.V
• Subjects: Angina; Angiography; Artificial intelligence; Attenuation; Cardiac; Computation; Computed tomography; Constraining; Diagnostic Radiology; Diagnostic systems; Hemodynamics; Imaging; Internal Medicine; Interventional Radiology; Ischemia; Learning algorithms; Lesions; Machine learning; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Multivariate analysis; Neuroradiology; Radiology; Risk; Stenosis; Subgroups; Ultrasound; Computed tomography angiography; Myocardial fractional flow reserve; Adipose tissue; Atheroma; Coronary artery disease
• DOI: 10.1007/s00330-019-06400-8
• ISSN: 0938-7994

Objective This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. Methods Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning–based FFR CT , and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant. Results This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFR CT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p  < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFR CT (AUC, 0.821 vs 0.850; p  = 0.426). The diagnostic accuracy of FFR CT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p  = 0.093). Conclusions Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy. Key Points • Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. • Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning–based FFR CT for predicting ischemic coronary stenosis. • No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features.