Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥1000) undergoing 18F‑FDG PET/CT
Elisabeth Sartoretti, AntonioG.Gennari, Alexander Maurer, Thomas Sartoretti, Stephan Skawran, Moritz Schwyzer, Alexia Rossi, AndreasA. Giannopoulos, Ronny R. Buechel, CatherineGebhard, MartinW. Huellner, Michael Messerli
This study aimed to identify and quantify high coronary artery calcium (CAC) using 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 and divided into two groups. The fully automated DL-based CACS tool, AVIEW CAC (Coreline Soft, v1.1.42), was used to perform CACS on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. The results showed that the DL tool underestimated CAC load but correctly assigned an Agatston score ≥ 1000 in over 70% of cases, provided sufficient CT image quality. In the control group, the DL tool did not generate false-positives.