المؤلفون: Sartoretti, Elisabeth^1,2 (AUTHOR), Gennari, Antonio G.^1,2 (AUTHOR), Maurer, Alexander^1,2 (AUTHOR), Sartoretti, Thomas^1,2 (AUTHOR), Skawran, Stephan^1,2 (AUTHOR), Schwyzer, Moritz^2,3,4 (AUTHOR), Rossi, Alexia^1,2 (AUTHOR), Giannopoulos, Andreas A.^1,2 (AUTHOR), Buechel, Ronny R.^1,2 (AUTHOR), Gebhard, Catherine^1,2,5 (AUTHOR), Huellner, Martin W.^1,2 (AUTHOR), Messerli, Michael^1,2 (AUTHOR) michael.messerli@usz.ch

المصدر: Scientific Reports. 11/10/2022, Vol. 12 Issue 1, p1-7. 7p.

مصطلحات موضوعية: *CORONARY artery calcification, *MYOCARDIAL perfusion imaging, *COMPUTED tomography, *CARDIOGRAPHIC tomography, *CANCER patients, *DEEP learning, *FLUORODEOXYGLUCOSE F18

مستخلص: 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. [ABSTRACT FROM AUTHOR]

المؤلفون: Morf, Claudia, Sartoretti, Thomas, Gennari, Antonio G., Maurer, Alexander, Skawran, Stephan, Giannopoulos, Andreas A., Sartoretti, Elisabeth, Schwyzer, Moritz, Curioni-Fontecedro, Alessandra, Gebhard, Catherine, Buechel, Ronny R., Kaufmann, Philipp A., Huellner, Martin W., Messerli, Michael

المصدر: Diagnostics (2075-4418); Aug2022, Vol. 12 Issue 8, p1876-N.PAG, 11p

مصطلحات موضوعية: CORONARY artery calcification, MYOCARDIAL perfusion imaging, ARTIFICIAL intelligence, FLUORODEOXYGLUCOSE F18, COMPUTED tomography

مستخلص: Objectives: The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. Methods: A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. Results: On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. Conclusions: Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings. [ABSTRACT FROM AUTHOR]

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