Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Journal: Scientific reports
PMID: 36357446

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.

Authors

  • Elisabeth Sartoretti
    Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
  • Antonio G Gennari
    Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
  • Alexander Maurer
    Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
  • Thomas Sartoretti
    Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
  • Stephan Skawran
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, CH-8091 Zurich, Switzerland; University of Zurich, Zurich, Switzerland.
  • Moritz Schwyzer
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
  • Alexia Rossi
    Department of Biomedical Sciences, Humanitas University, Milan, Pieve Emanuele, Italy.
  • Andreas A Giannopoulos
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • Ronny R Buechel
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: ronny.buechel@usz.ch.
  • Catherine Gebhard
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: catherine.gebhard@usz.ch.
  • Martin W Huellner
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland.
  • Michael Messerli
    Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Switzerland. Electronic address: michael.messerli@usz.ch.

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