Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis UsingInvasive Angiography as Reference Standard
AJR. American journal of roentgenology
Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. 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 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 CREDENENCE trial. The study included 301 patients [mean age 64.4±10.2 years; 88 female, 213 male] recruited from 2014 to 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 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%, including severe (≥70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified non-obstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p<.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). AI-QCT ≥50% 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 AI-QCT 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. ClinicalTrials.gov NCT02173275.
Lipkin, Isabella; Telluri, Anha; Kim, Yumin; Sidahmed, Alfateh; Krepp, Joseph M.; Choi, Brian G.; Jonas, Rebecca; Marques, Hugo; Chang, Hyuk-Jae; Choi, Jung Hyun; Doh, Joon-Hyung; Her, Ae-Young; Koo, Bon-Kwon; Nam, Chang-Wook; Park, Hyung-Bok; Shin, Sang-Hoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lu, Bin; Gao, Yang; Nabi, Faisal; Nakazato, Ryo; Schoepf, U Joseph; Driessen, Roel S.; Bom, Michiel J.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; and Knaapen, Paul, "Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis UsingInvasive Angiography as Reference Standard" (2022). GW Authored Works. Paper 767.