Optimizing AI-Derived CAC Volume Thresholds on Non-Gated Chest CT for Detection of Actionable Coronary Artery Calcium: A Validation Against Agatston Scoring and Radiologist Reporting

PURPOSE: Coronary artery calcium (CAC) visually estimated on non-electrocardiogram (ECG)-gated chest CTs is reported inconsistently, limiting identification of high-risk individuals. Although automated AI-derived CAC scoring offers the potential to standardize reporting and improve reliability, this method warrants rigorous validation. MATERIALS AND METHODS: Three hundred thirty-two patients (59% female, median age 62) who underwent a non-gated chest CT within 1-year of a dedicated cardiac CT with Agatston's CAC score (CACscore) were identified between 2016 and 2022. AI-Rad Companion (Siemens) was used to compute CAC volume (AI-CACvol). AI-CACvol performance was compared with radiologists' qualitative CAC reports (H-CACqual) using optimized thresholds for identifying actionable CAC. Model performance was further validated in a postimplementation population. RESULTS: Median AI-CACvol was 20.3 mm³ (IQR 0-188.65), strongly correlated with CACscore (Spearman ρ=0.91, 95% CI: 0.88-0.94); median 31.55 Agatston Units (AU), IQR: 0 to 250). Agreement with CACscore was higher for AI-CACvol versus H-CACqual (weighted kappa 0.88, 95% CI: 0.84, 0.91; vs. 0.85, 95% CI: 0.82, 0.88; P=0.022). At a cutoff of 70 mm³, AI-CACvol demonstrated superior sensitivity compared with H-CACqual for detecting CAC≥100 AU, with greater sensitivity (95% vs. 75%) and comparable specificity (92% vs. 94%). In a validation cohort of 39 patients (64% female, median age 67 y), median CACscore was 43 AU (IQR 0-215) and median AI-CACvol 29.7 mm³ (IQR: 0 to 219.4). The same 70 mm³ cutoff achieved 100% sensitivity and 88% specificity for CAC≥100 AU (n=13). CONCLUSION: AI-derived CAC volume from non-gated chest CTs correlates strongly with reference CAC scores, showing high sensitivity at actionable thresholds. This supports potential opportunistic screening, though impact on outcomes requires study.

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Journal
Journal of Thoracic Imaging
Published
2026-11-11
DOI
https://doi.org/10.1097/rti.0000000000000917
Primary Topic
Cardiac Imaging and Diagnostics
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article
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article

Optimizing AI-Derived CAC Volume Thresholds on Non-Gated Chest CT for Detection of Actionable Coronary Artery Calcium: A Validation Against Agatston Scoring and Radiologist Reporting

Xinhui Duan, Travis G. Browning, Maya Paryente Wiessman, Suhny Abbara et al.
Journal of Thoracic Imaging
Cardiac Imaging and Diagnostics
article

Optimizing AI-Derived CAC Volume Thresholds on Non-Gated Chest CT for Detection of Actionable Coronary Artery Calcium: A Validation Against Agatston Scoring and Radiologist Reporting

Xinhui Duan, Travis G. Browning, Maya Paryente Wiessman, Suhny Abbara, Ann Marie Návar, Ronald M. Peshock, Arzu Canan, Parag Joshi, James Wyatt Miller, Yin Xi, Fernando Kay
article en

Abstract

PURPOSE: Coronary artery calcium (CAC) visually estimated on non-electrocardiogram (ECG)-gated chest CTs is reported inconsistently, limiting identification of high-risk individuals. Although automated AI-derived CAC scoring offers the potential to standardize reporting and improve reliability, this method warrants rigorous validation. MATERIALS AND METHODS: Three hundred thirty-two patients (59% female, median age 62) who underwent a non-gated chest CT within 1-year of a dedicated cardiac CT with Agatston's CAC score (CACscore) were identified between 2016 and 2022. AI-Rad Companion (Siemens) was used to compute CAC volume (AI-CACvol). AI-CACvol performance was compared with radiologists' qualitative CAC reports (H-CACqual) using optimized thresholds for identifying actionable CAC. Model performance was further validated in a postimplementation population. RESULTS: Median AI-CACvol was 20.3 mm³ (IQR 0-188.65), strongly correlated with CACscore (Spearman ρ=0.91, 95% CI: 0.88-0.94); median 31.55 Agatston Units (AU), IQR: 0 to 250). Agreement with CACscore was higher for AI-CACvol versus H-CACqual (weighted kappa 0.88, 95% CI: 0.84, 0.91; vs. 0.85, 95% CI: 0.82, 0.88; P=0.022). At a cutoff of 70 mm³, AI-CACvol demonstrated superior sensitivity compared with H-CACqual for detecting CAC≥100 AU, with greater sensitivity (95% vs. 75%) and comparable specificity (92% vs. 94%). In a validation cohort of 39 patients (64% female, median age 67 y), median CACscore was 43 AU (IQR 0-215) and median AI-CACvol 29.7 mm³ (IQR: 0 to 219.4). The same 70 mm³ cutoff achieved 100% sensitivity and 88% specificity for CAC≥100 AU (n=13). CONCLUSION: AI-derived CAC volume from non-gated chest CTs correlates strongly with reference CAC scores, showing high sensitivity at actionable thresholds. This supports potential opportunistic screening, though impact on outcomes requires study.

Journal of Thoracic Imaging
Mayo Clinic (US), The University of Texas MD Anderson Cancer Center (US), Tel Aviv University (IL), Mayo Clinic in Florida (US), The University of Texas Southwestern Medical Center (US)
Cardiac Imaging and Diagnostics
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