Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Journal: Investigative radiology
Published Date: Oct 23, 2024

Abstract

PURPOSE: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance.

Authors

  • Judit Simon
    MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary.
  • Peter Mikhael
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Mass.
  • Alexander Graur
  • Allison E B Chang
    Department of Medicine, Massachusetts General Hospital, Boston, MA.
  • Steven J Skates
  • Raymond U Osarogiagbon
  • Lecia V Sequist
    Harvard Medical School, Boston, MA.
  • Florian J Fintelmann
    Department of Radiology, Massachusetts General Hospital, Boston, MA.

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