High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

Journal: AJR. American journal of roentgenology
PMID: 37132550

Abstract

Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation ( = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Authors

  • Andreas A Giannopoulos
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • Lukas Keller
    Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.
  • Daniel Sepulcri
    Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.
  • Reto Boehm
    Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.
  • Chrysoula Garefa
    Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.
  • Prem Venugopal
    Queensland Paediatric Cardiac Service (QPCS), Queensland Children's Hospital, Brisbane, Qld, Australia; Children's Health Research Centre, University of Queensland, Brisbane, Qld, Australia; School of Clinical Medicine, Children's Health Queensland Clinical Unit, University of Queensland, Brisbane, Qld, Australia.
  • Jhimli Mitra
    Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
  • Soumya Ghose
    Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States.
  • Paul Deak
    GE Healthcare, Glattbrugg, Switzerland.
  • Jed D Pack
    GE Research, Schenectady, NY.
  • Cynthia L Davis
    GE Research, Schenectady, NY.
  • Barbara E Stähli
    Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland.
  • Julia Stehli
    Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland.
  • Aju P Pazhenkottil
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: aju.pazhenkottil@usz.ch.
  • Philipp A Kaufmann
    Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
  • Ronny R Buechel
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: ronny.buechel@usz.ch.

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