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

Abstract

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.

Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification

“…However, given these test characteristics, this technology would be unlikely to serve as a strong screening tool prior to CAC in its current state. Fully automated CAC scoring has also been demonstrated from noncontrast ungated scans from 18F-FDG-PET/CT and does not require changes to the PET/CT scanning protocol [68,69].…”

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes

“…However, the agreement of risk categorization between AI and expert evaluation varied according to the test dataset used, being between k = 0.58 and k = 0.80 on routine chest CT using external datasets [ 83 ] and between k = 0.85 [ 93 ] and k = 0.91 [ 74 ] on low-dose chest CT using internal ones. Similarly, the agreement between risk categories based on ECG-gated and non-gated images ranked from k = 0.52 to 0.82, with lower values obtained with external datasets [ 83 , 95 ]. This proves the important connection between the algorithm’s performance and the dataset-specific characteristics (i.e., scanner, field-of-view, reconstruction filter, slice thickness, etc.)…”

“…Besides these drawbacks, a recent study on 5,678 adults without known ASCVD transitioned AI usage from research into a clinical scenario by utilizing a DL-based algorithm to analyze CACS on non-gated images, showing that adults with DL-CACS ≥ 100 had an increased risk of death (adjusted hazard ratio: 1.51; 95% CI: 1.28 to 1.79) compared to those with DL-CACS 0 [ 97 ]. A fascinating, additional perspective was provided by a cloud-based DL CACS evaluator showing high ICC value (0.88; 95% CI: 0.83 to 0.92) between ECG-gated and non-gated images derived from 18 F-fluorodeoxyglucose positron emission tomography (PET) [ 95 ]. Although these results were not replicated in a similar setting [ 98 ], cloud-based tools have the potential to broaden the users of AI-based CACS evaluation beyond university and tertiary hospitals, helping to reach its full potential.…”

Artificial Intelligence in Coronary Artery Calcium Scoring

“…One study used a U-Net-based model, AVIEW CAC by Coreline Soft, for automatic CACS in ungated CT scans from 100 patients who underwent 18-FDG PET/CT [ 27 ]. Patients in this study also underwent ECG-gated CT scans within 6 months of PET/CT that were manually scored and compared to the AI-based CACS from ungated CT.…”