End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

Journal: JCO clinical cancer informatics
Published Date: Oct 1, 2021

Abstract

PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification.

Authors

  • Felipe Soares Torres
    Joint Department of Medical Imaging, Toronto General Hospital, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Shazia Akbar
    Altis Labs, Inc, Toronto, ON, Canada.
  • Srinivas Raman
    Department of Radiation Oncology, BC Cancer Vancouver, 600 W 10th Ave, Vancouver, BC, V5Z 4E6, Canada, 1 416-946-4501.
  • Kazuhiro Yasufuku
    Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Ontario, Canada. Electronic address: kazuhiro.yasufuku@uhn.ca.
  • Carola Schmidt
    Altis Labs, Inc, Toronto, ON, Canada.
  • Ahmed Hosny
    Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Felix Baldauf-Lenschen
    Altis Labs, Inc, Toronto, ON, Canada.
  • Natasha B Leighl
    Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

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