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.