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Updated: Jun 16, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Plaque burden improves the detection of ischemic CAD over stenosis from coronary computed tomography angiography
Tanja Kero1,2, Sarah Bär3,4, Antti Saraste3,5
1Department of Surgical Sciences, Nuclear Medicine & PET, Uppsala University, Uppsala, Sweden. tanja.kero@uu.se.
Insights
Quantifying plaque burden using percent atheroma volume (PAV) with coronary CT angiography (CTA) improves detection of ischemic coronary artery disease (CAD). This AI-guided approach offers incremental value beyond stenosis severity and clinical factors for patient-level diagnosis.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- Coronary artery disease (CAD) diagnosis relies on assessing luminal narrowing, but plaque burden may offer additional insights.
- Coronary computed tomography angiography (CTA) is widely used for CAD evaluation.
- Identifying ischemic CAD accurately is crucial for guiding patient management.
Purpose of the Study:
- To evaluate if quantifying plaque burden via AI-guided quantitative CT (AI-QCT) provides incremental diagnostic value for ischemic CAD.
- To compare the diagnostic performance of AI-QCT plaque metrics against traditional stenosis assessment and clinical risk factors.
- To determine the optimal application of plaque quantification for improved patient-level CAD detection.
Main Methods:
- 2145 symptomatic patients with suspected CAD underwent coronary CTA and 15O-water PET myocardial perfusion imaging.
- AI-QCT was used to measure maximum diameter stenosis, percent atheroma volume (PAV), percent calcified plaque volume (CPV), and percent noncalcified plaque volume (NCPV).
- Ischemic CAD was defined by abnormal stress perfusion on PET; diagnostic performance was assessed using Area Under the Curve (AUC) and predictive values.
Main Results:
- Percent atheroma volume (PAV) significantly improved the prediction of ischemic CAD compared to clinical variables and stenosis alone (AUC 0.91 vs. 0.87).
- Applying a PAV cut-off of 12.2% in patients with intermediate stenosis (30-70%) yielded the best diagnostic performance (88% accuracy).
- This approach demonstrated high sensitivity (76%), specificity (91%), and negative predictive value (95%) for detecting ischemic CAD.
Conclusions:
- Quantitative plaque burden assessment using AI-QCT, specifically PAV, offers incremental value for identifying ischemic CAD in symptomatic patients.
- Integrating PAV measurement into coronary CTA analysis enhances diagnostic accuracy beyond traditional stenosis assessment.
- A PAV threshold of 12.2% in intermediate stenosis cases provides optimal performance for detecting PET-defined ischemic CAD.
Abstract:
In symptomatic patients undergoing coronary CTA for suspected coronary artery disease (CAD), we assessed if quantification of plaque burden, in addition to luminal narrowing and clinical risk factors, offers incremental value for the identification of ischemic CAD on a per patient level. We evaluated 2145 patients who underwent coronary CTA for suspected CAD with sequential selective downstream 15O-water positron emission tomography (PET) myocardial perfusion imaging. Coronary CTA scans were analyzed using Artificial Intelligence-guided Quantitative Computed Tomography (AI-QCT), with measurement of maximum diameter stenosis, percent atheroma volume (PAV), percent calcified plaque volume (CPV) and percent noncalcified plaque volume (NCPV). Ischemic CAD was defined as the presence of abnormal stress perfusion on 15O-water PET. PAV on top of the clinical variables and ≥ 50% stenosis improved the prediction of ischemic CAD on a per patient level as compared to clinical variables and ≥ 50% stenosis (AUC = 0.91 vs. AUC = 0.87, p < 0.001). The best diagnostic performance was achieved when PAV with a cut-off value of 12.2% was applied in patients with intermediate (30-70%) stenosis; using this approach, the sensitivity, specificity, positive and negative predictive values and diagnostic accuracy for ischemic CAD were 76%, 91%, 64%, 95% and 88%. The addition of quantitative plaque volume on top of clinical variables and ≥ 50% diameter stenosis improves the detection of ischemic CAD as defined by PET perfusion imaging. Applying a PAV threshold of 12.2% in patients with intermediate stenosis provided the best diagnostic performance.
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