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

Research output: Contribution to journalArticleAcademicpeer-review

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

Cite this

Kather, J. N., Pearson, A. T., Halama, N., Jaeger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., Grabsch, H. I., Yoshikawa, T., Brenner, H., Chang-Claude, J., Hoffmeister, M., Trautwein, C., & Luedde, T. (2019). Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature Medicine, 25(7), 1054-+. https://doi.org/10.1038/s41591-019-0462-y