BACKGROUND: Coronary plaque features are imaging biomarkers of cardiovascular risk, but less is known about sex-specific patterns in their prognostic value. This study aimed to define sex differences in the coronary atherosclerotic phenotypes assessed by artificial intelligence–based quantitative computed tomography (AI-QCT) and the associated risk of major adverse cardiovascular events (MACEs). METHODS: Global multicenter registry including symptomatic patients with suspicion of coronary artery disease referred for coronary computed tomography angiography. AI-QCT analyzed 16 coronary artery disease features. The primary end point was MACE defined as death, myocardial infarction, late revascularization, cerebrovascular events, unstable angina, and congestive heart failure. RESULTS: Among 3551 patients (mean age, 59±12 years; 49.5% women), MACE occurred in 3.2% of women and 6.1% of men during an average follow-up of 4.8±2.2 years. The AI-QCT features total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume were significantly higher in men ( P <0.001), and high-risk plaques were more prevalent (9.2% versus 2.5%; P <0.0001). Independent of age and cardiovascular risk factors, the AI-QCT-derived features of total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume conferred a higher relative risk of MACE in women than men. For every 50-mm ³ increase in total plaque volume, relative risk increased by 17.7% (95% CI, 1.12–1.24) in women versus 5.3% (95% CI, 1.03–1.07) in men ( P _interaction <0.001); for noncalcified plaque, relative risk increased by 27.1% (95% CI, 1.17–1.38) versus 11.6% (95% CI, 1.08–1.15; P _interaction =0.0015); and for calcified plaque, relative risk increased by 22.9% (95% CI, 1.14–1.33) versus 5.4% (95% CI, 1.01–1.10; P _interaction =0.0012), respectively. Similarly, for percentage atheroma volume, the risk was higher in women. The findings remained unchanged when restricted to a secondary composite end point (death and myocardial infarction). CONCLUSIONS: The AI-QCT plaque features, total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume, conferred a higher relative MACE risk in women and may prompt more aggressive antiatherosclerotic therapy and reinforced preventive interventions. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04279496.

Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI_4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP_<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm³. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm³, versus 42%, 100%, and 27% using 0 mm³ volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r² = 0.87), lumen area (r² = 0.87), plaque burden (r² = 0.78) and lesion length (r² = 0.88), respectively. AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI_4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.