Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.

Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Published Date: Jan 12, 2023

Abstract

PURPOSE: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

Authors

  • Peter G Mikhael
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Jeremy Wohlwend
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
  • Adam Yala
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Ludvig Karstens
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
  • Justin Xiang
    Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • Angelo K Takigami
    Harvard Medical School, Boston, MA.
  • Patrick P Bourgouin
    Harvard Medical School, Boston, MA.
  • PuiYee Chan
    Department of Medicine, Massachusetts General Hospital, Boston, MA.
  • Sofiane Mrah
    Department of Radiology, Massachusetts General Hospital, Boston, MA.
  • Wael Amayri
    Department of Radiology, Massachusetts General Hospital, Boston, MA.
  • Yu-Hsiang Juan
    Chang Gung University, Taoyuan, Taiwan.
  • Cheng-Ta Yang
    Chang Gung University, Taoyuan, Taiwan.
  • Yung-Liang Wan
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Gigin Lin
    Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Lecia V Sequist
    Harvard Medical School, Boston, MA.
  • Florian J Fintelmann
    Department of Radiology, Massachusetts General Hospital, Boston, MA.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.

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