Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: May 1, 2020

Abstract

OBJECTIVE: Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively.

Authors

  • Kun-Hsing Yu
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Feiran Wang
    Stanford University.
  • Gerald J Berry
    Department of Pathology, Stanford University, Stanford, California, USA.
  • Christopher Ré
    1Stanford University, Stanford, CA USA.
  • Russ B Altman
    Departments of Medicine, Genetics and Bioengineering, Stanford University, Stanford, California, United States of America.
  • Michael Snyder
    Department of Genetics, Stanford University School of Medicine, Stanford, Calif.
  • Isaac S Kohane
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Isaac_Kohane@hms.harvard.edu.

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