Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT

Abstract

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool’s capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.

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

“…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.…”

“…Algorithm architecture, use of graphic processing unit, and the number of cores of the computer processing unit strongly impacted the computational time taken to quantify CACS, generating heterogeneous results (mean computational time: 3 min, range: 2 s to 10 min, Fig. 4 ) [ 74 , 81 , 83 – 86 , 96 , 98 – 100 ]. Irrespective of the computational time, these results show that automatizing CACS calculation may reduce its costs and streamline the workflow of imaging departments.…”