部分流量储备
医学
冠状动脉疾病
狭窄
放射科
血管造影
计算机断层血管造影
计算机断层血管造影
冠状动脉
心脏病学
冠状动脉造影
动脉
核医学
心肌梗塞
作者
Adriaan Coenen,Young-Hak Y.-H. Kim,Mariusz Kruk,Christian Tesche,Jakob J. De Geer,Akira Kurata,Marisa Lubbers,Joost Daemen,Lucian Itu,Saikiran Rapaka,Puneet Sharma,Chris Schwemmer,Anders Persson,Joseph Schoepf,Cezary Kępka,Dong Hyun Yang,Koen Nieman
出处
期刊:Circulation-cardiovascular Imaging
[Ovid Technologies (Wolters Kluwer)]
日期:2018-06-01
卷期号:11 (6)
被引量:282
标识
DOI:10.1161/circimaging.117.007217
摘要
Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease.At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P<0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR.On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.
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