Artificial-intelligence-assisted CCTA quantifies sex differences in coronary atherosclerotic burden at low atheroma volumes
Background: Coronary artery disease (CAD) manifests differently between sexes, with data suggesting females develop more non-calcified plaques that traditional calcium-centric tools may not detect. Methods: We conducted a retrospective cohort study of 100 individuals with low total atheroma volume (...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | International Journal of Cardiology: Heart & Vasculature |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352906725001617 |
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| Summary: | Background: Coronary artery disease (CAD) manifests differently between sexes, with data suggesting females develop more non-calcified plaques that traditional calcium-centric tools may not detect. Methods: We conducted a retrospective cohort study of 100 individuals with low total atheroma volume (TAV) < 250 mm3 using artificial intelligence (AI)-enabled coronary computed tomography angiography (CCTA) to assess sex-based differences in coronary plaque composition. Plaque subtypes included calcified, non-calcified, and low-density non-calcified atheroma volumes. Results: Females had significantly lower total (p = 0.018) and non-calcified plaque (p < 0.001) burden compared to males. Calcified (p = 0.52) and low-density non-calcified (p = 0.16) plaque volumes did not differ significantly. Age was a consistent predictor of plaque volume across most subtypes. Conclusions: Despite low overall plaque burden, males demonstrated a higher non-calcified plaque burden than females. This finding contrasts with previous literature and underscores the potential of AI-enabled CCTA to detect subclinical coronary disease, particularly in low-risk cohorts. These results support the use of comprehensive plaque profiling in both sexes to improve early risk stratification. |
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| ISSN: | 2352-9067 |