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 e

SUBMITTER: Sartoretti E 

PROVIDER: S-EPMC9649723 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature