ArticleScientific reports2022
Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.European radiology · 2025Article
- Advances in Artificial Intelligence for Non-ECG-Gated Coronary Artery Calcium Scoring: A Scoping Review.Cureus · 2025Review
- Incidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?Current atherosclerosis reports · 2025Review
- Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.European heart journal. Cardiovascular Imaging · 2024Article
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
- A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.Translational cancer research · 2023Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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