Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

• Published in: Diagnostics (Basel) 2022-08, Vol.12 (8), p.1876
• Main Authors: Morf, Claudia, Sartoretti, Thomas, Gennari, Antonio G., Maurer, Alexander, Skawran, Stephan, Giannopoulos, Andreas A., Sartoretti, Elisabeth, Schwyzer, Moritz, Curioni-Fontecedro, Alessandra, Gebhard, Catherine, Buechel, Ronny R., Kaufmann, Philipp A., Huellner, Martin W., Messerli, Michael
• Format: Article
• Language: English
• Subjects: Accuracy; Agreements; Artificial intelligence; Automation; Calcium; Cancer; Cardiovascular disease; Coronary arteries; coronary artery calcium scoring; coronary artery disease; Coronary vessels; CT imaging; Datasets; deep learning; Diagnostic imaging; Electrocardiogram; Electrocardiography; Health aspects; Medical imaging; Medical imaging equipment; Methods; Mortality; Patients; PET imaging; Population; positron emission tomography; Regression analysis; Software; Tomography; Veins & arteries
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12081876

Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.