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Published on: August 9, 2024
Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive
Isabella Lipkin1, Anha Telluri1, Yumin Kim1
1The George Washington University School of Medicine and Health Sciences, 2150 Pennsylvania Ave NW, Ste 4-417, Washington, DC 20037.
Insights
Artificial intelligence quantitative CT (AI-QCT) demonstrated superior diagnostic performance compared to myocardial perfusion imaging (MPI) for detecting obstructive coronary artery disease (CAD). Incorporating AI-QCT into diagnostic algorithms can significantly reduce unnecessary invasive angiography procedures.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Radiology
Background:
- Deep learning frameworks are increasingly used for interpreting coronary computed tomography angiography (CTA) in coronary artery disease (CAD) evaluation.
- Current diagnostic pathways for obstructive CAD often involve invasive angiography, which carries risks and costs.
- Comparing advanced non-invasive imaging techniques is crucial for optimizing patient management.
Purpose of the Study:
- To compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA interpreted with artificial intelligence quantitative CT (AI-QCT) for detecting obstructive CAD.
- To evaluate the impact of integrating AI-QCT into diagnostic algorithms on downstream invasive testing utilization.
Main Methods:
- Retrospective analysis of the CREDENCE trial cohort (301 patients) undergoing coronary CTA, MPI, and invasive angiography (quantitative coronary angiography [QCA] and fractional flow reserve [FFR]).
- Coronary CTA images were analyzed using an FDA-cleared AI-QCT software for stenosis determination.
- Diagnostic performance metrics (AUC) and downstream invasive angiography utilization scenarios were compared.
Main Results:
- AI-QCT showed significantly higher AUC than MPI for detecting obstructive stenosis (≥50% QCA: 0.88 vs 0.66; ≥70% QCA: 0.92 vs 0.81; FFR <0.80: 0.90 vs 0.71; all p < .001).
- AI-QCT identified obstructive stenosis in 54% of patients without ischemia on MPI.
- Diagnostic algorithms incorporating AI-QCT could reduce invasive angiography utilization by 39-49% compared to current strategies.
Conclusions:
- Coronary CTA with AI-QCT exhibits superior diagnostic performance over MPI for identifying obstructive CAD.
- AI-QCT analysis holds significant potential to streamline diagnostic pathways, reducing unnecessary invasive procedures and associated costs.
Abstract:
BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. 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. METHODS. 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. RESULTS. 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 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). 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%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.
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