Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia

ObjectivesWe sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.MethodsEighty-four patients (6110years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.ResultsOne hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p=0.037), non-calcified plaque volume (OR 1.02, p=0.007), napkin-ring sign (OR 5.97, p=0.014), and CT-FFR (OR 0.81, p