Title

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard

Authors

Isabella Lipkin, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Anha Telluri, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Yumin Kim, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Alfateh Sidahmed, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Joseph M. Krepp, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Brian G. Choi, The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Rebecca Jonas, Jefferson Medical Institute, Philadelphia, PA.
Hugo Marques, Faculdade de Medicina da Universidade Católica Portuguesa, Lisboa, Portugal.
Hyuk-Jae Chang, Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.
Jung Hyun Choi, Ontact Health, Inc., Seoul, South Korea.
Joon-Hyung Doh, Division of Cardiology, Inje University Ilsan Paik Hospital, Goyang-si, South Korea.
Ae-Young Her, Kang Won National University Hospital, Chuncheon, South Korea.
Bon-Kwon Koo, Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.
Chang-Wook Nam, Cardiovascular Center, Keimyung University Dongsan Hospital, Daegu, South Korea.
Hyung-Bok Park, Department of Internal Medicine, Division of Cardiology, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea.
Sang-Hoon Shin, Department of Internal Medicine, Division of Cardiology, Ewha Women's University Seoul Hospital, Seoul, South Korea.
Jason Cole, Mobile Cardiology Associates, Mobile, AL.
Alessia Gimelli, Department of Imaging, Fondazione Toscana Gabriele Monasterio, Pisa, Italy.
Muhammad Akram Khan, Cardiac Center of Texas, McKinney, TX.
Bin Lu, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Beijing, China.
Yang Gao, Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan.
Faisal Nabi, Houston Methodist DeBakey Heart and Vascular Center, Houston, TX.
Ryo Nakazato, Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan.
U Joseph Schoepf, Medical University of South Carolina, Charleston, SC.
Roel S. Driessen, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands.
Michiel J. Bom, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands.
James J. Jang, San Jose Medical Center, Kaiser Permanente Hospital, San Jose, CA.
Michael Ridner, Heart Center Research, LLC, Huntsville, AL.
Chris Rowan, Renown Heart and Vascular Institute, Reno, NV.
Erick Avelar, Oconee Heart and Vascular Center, St. Mary's Hospital, Athens, GA.
Philippe Généreux, Gagnon Cardiovascular Institute at Morristown Medical Center, Morristown, NJ.
Paul Knaapen, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands.

Document Type

Journal Article

Publication Date

6-15-2022

Journal

AJR. American journal of roentgenology

DOI

10.2214/AJR.21.27289

Keywords

CCTA; artificial intelligence; atherosclerosis; coronary CT; coronary CTA; coronary artery disease; fractional flow reserve; quantitative coronary angiography

Abstract

Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. Clinicaltrials.gov NCT02173275.

Department

Medicine

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