Adriaan Coenen1,2, Young-Hak Kim3, Mariusz Kruk4, Christian Tesche5, Jakob De Geer6, Akira Kurata2,7, Marisa L Lubbers8,2, Joost Daemen8, Lucian Itu9, Saikiran Rapaka10, Puneet Sharma10, Chris Schwemmer11, Anders Persson6, U Joseph Schoepf5, Cezary Kepka4, Dong Hyun Yang12, Koen Nieman8,2,13. 1. Department of Cardiology (A.C., M.L.L., J.D., K.N.) a.coenen@erasmusmc.nl. 2. Department of Radiology (A.C., A.K., M.L.L., K.N.). 3. Erasmus University Medical Center, Rotterdam, the Netherlands. Department of Cardiology, Heart Institute (Y.-H.K.). 4. Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. Coronary Disease and Structural Heart Diseases Department, Institute of Cardiology, Warsaw, Poland (M.K., C.K.). 5. Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston (C.T., U.J.S.). 6. Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, Linköping University, Sweden (J.D.G., A.P.). 7. Department of Radiology, Ehime University Graduate School of Medicine, Japan (A.K.). 8. Department of Cardiology (A.C., M.L.L., J.D., K.N.). 9. Corporate Technology, Siemens SRL, Brasov, Romania (L.I.). 10. Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ (S.R., P.S.). 11. Computed Tomography-Research & Development, Siemens Healthcare GmbH, Forchheim, Germany (C.S.). 12. Department of Radiology (D.H.Y.). 13. Stanford University School of Medicine, Cardiovascular Institute, Stanford, CA, USA (K.N.).
Abstract
BACKGROUND: 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. METHODS AND RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS AND RESULTS: 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. CONCLUSIONS: 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.
Authors: Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick Journal: J Am Coll Cardiol Date: 2019-03-26 Impact factor: 24.094
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Authors: Axel Meineke; Christian Rubbert; Lino M Sawicki; Christoph Thomas; Yan Klosterkemper; Elisabeth Appel; Julian Caspers; Oliver T Bethge; Patric Kröpil; Gerald Antoch; Johannes Boos Journal: Eur Radiol Date: 2019-02-19 Impact factor: 5.315