Deep learning links histology, molecular signatures and prognosis in cancer.

Journal: Nature cancer
Published Date:

Abstract

Deep learning can be used to predict genomic alterations based on morphological features learned from digital histopathology. Two independent pan-cancer studies now show that automated learning from digital pathology slides and genomics can potentially decipher broader classes of molecular signatures and prognostic associations across cancer types.

Authors

  • Nicolas Coudray
    Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA. nicolas.coudray@nyulangone.org.
  • Aristotelis Tsirigos
    Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA. Electronic address: aristotelis.tsirigos@nyulangone.org.