Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Jakob Nikolas Kather*, Alexander T. Pearson, Niels Halama, Dirk Jaeger, Jeremias Krause, Sven H. Loosen, Alexander Marx, Peter Boor, Frank Tacke, Ulf Peter Neumann, Heike I. Grabsch, Takaki Yoshikawa, Hermann Brenner, Jenny Chang-Claude, Michael Hoffmeister, Christian Trautwein, Tom Luedde*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

352 Citations (Web of Science)

Abstract

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.

Original languageEnglish
Pages (from-to)1054-+
Number of pages8
JournalNature Medicine
Volume25
Issue number7
DOIs
Publication statusPublished - Jul 2019

Keywords

  • COMPREHENSIVE MOLECULAR CHARACTERIZATION
  • GASTRIC-CANCER
  • TUMORS

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