Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

被引：200

作者：

Tesche, Christian ^{[1
,6
]}

De Cecco, Carlo N. ^{[1
,7
]}

Baumann, Stefan ^{[1
,8
]}

Renker, Matthias ^{[1
,9
]}

McLaurin, Tindal W. ^{[1
]}

Duguay, Taylor M. ^{[1
]}

Bayer, Richard R., II ^{[1
,2
]}

Steinberg, Daniel H. ^{[2
]}

Grant, Katharine L. ^{[3
]}

Canstein, Christian ^{[3
]}

Schwemmer, Chris ^{[3
]}

Schoebinger, Max ^{[3
]}

Itu, Lucian M. ^{[4
]}

Rapaka, Saikiran ^{[5
]}

Sharma, Puneet ^{[5
]}

Schoepf, U. Joseph ^{[1
,2
]}

机构：

[1] Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Ashley River Tower,25 Courtenay Dr, Charleston, SC 29425 USA

[2] Med Univ South Carolina, Dept Med, Div Cardiol, Ashley River Tower,25 Courtenay Dr, Charleston, SC 29425 USA

[3] Siemens Healthcare GmbH, Dept Computed Tomog Res & Dev, Forchheim, Germany

[4] Siemens SRL, Dept Corp Technol, Brasov, Romania

[5] Siemens Healthcare, Dept Med Imaging Technol, Princeton, NJ USA

[6] Heart Ctr Munich Bogenhausen, Dept Cardiol & Intens Care Med, Munich, Germany

[7] Sapienza Univ Rome, Dept Radiol Sci Oncol & Pathol, Rome, Italy

[8] Heidelberg Univ, Univ Med Ctr Mannheim UMM, Fac Med Mannheim, Dept Med 1, Mannheim, Germany

[9] Kerckhoff Heart Ctr, Dept Cardiol, Bad Nauheim, Germany

来源：

RADIOLOGY | 2018年 / 288卷 / 01期

关键词：

COMPUTED-TOMOGRAPHY ANGIOGRAPHY; DIAGNOSTIC PERFORMANCE; HEMODYNAMIC SIGNIFICANCE; BLOOD-FLOW; INTERMEDIATE; GUIDELINES; STANDARD; SOCIETY; LESIONS;

D O I：

10.1148/radiol.2018171291

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Purpose: To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods: A total of 85 patients (mean age, 62 years 6 +/- 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived onsite from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results: On a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P=.86 and P=.92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P<.0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P<.0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes 6 6.3 vs 43.4 minutes 6 +/- 7.1; P=.042). Conclusion: The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis. (c) RSNA, 2018

引用

页码：64 / 72

页数：9

共 26 条

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