Diagnostic Performance of AI-enabled Plaque Quantification from Coronary CT Angiography Compared with Intravascular Ultrasound.
Journal:
Radiology. Cardiothoracic imaging
PMID:
39540820
Abstract
Purpose To assess the diagnostic performance of a coronary CT angiography (CCTA) artificial intelligence (AI)-enabled tool (AI-QCPA; HeartFlow) to quantify plaque volume, as compared with intravascular US (IVUS). Materials and Methods A retrospective subanalysis of a single-center prospective registry study was conducted in participants with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention of the culprit vessel. Participants with greater than 50% stenosis in nonculprit vessels underwent CCTA, invasive coronary angiography, and IVUS of nonculprit lesion(s) between 2 and 40 days after primary percutaneous coronary intervention. Comparisons of plaque volumes obtained using AI-QCPA (HeartFlow) and IVUS were assessed using Spearman rank correlation (ρ) and Bland-Altman analysis. Results Thirty-three participants (mean age, 59.1 years ± 8.8 [SD]; 27 [82%] male and six [18%] female participants) and 67 vessels were included for analysis. There was strong agreement between AI-QCPA and IVUS in vessel (ρ = 0.94) and lumen volumes (ρ = 0.97). High agreement between AI-QCPA and IVUS was also found for total plaque volume (ρ = 0.92), noncalcified plaque (ρ = 0.91), and calcified plaque (ρ = 0.87). Bland-Altman analysis demonstrated AI-QCPA underestimated total plaque volume (-9.4 mm) and calcified plaque (-11.4 mm) and overestimated for noncalcified plaque (2.0 mm) when compared with IVUS. Conclusion An AI-enabled automated plaque quantification tool for CCTA had high agreement with IVUS for quantifying plaque volume and characterizing plaque. Coronary Plaque, Intravascular US, Coronary CT Angiography, Artificial Intelligence ClinicalTrials.gov registration no. NCT02926755 © RSNA, 2024.