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

doi:10.1038/s41591-019-0462-y

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 journal › Article › Academic › peer-review

204 Citations (Web of Science)

Original language	English
Pages (from-to)	1054-+
Number of pages	8
Journal	Nature Medicine
Volume	25
Issue number	7
DOIs	https://doi.org/10.1038/s41591-019-0462-y
Publication status	Published - Jul 2019

Keywords

COMPREHENSIVE MOLECULAR CHARACTERIZATION
GASTRIC-CANCER
TUMORS

Access to Document

10.1038/s41591-019-0462-y

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

Kather, Jakob Nikolas ; Pearson, Alexander T. ; Halama, Niels ; Jaeger, Dirk ; Krause, Jeremias ; Loosen, Sven H. ; Marx, Alexander ; Boor, Peter ; Tacke, Frank ; Neumann, Ulf Peter ; Grabsch, Heike I. ; Yoshikawa, Takaki ; Brenner, Hermann ; Chang-Claude, Jenny ; Hoffmeister, Michael ; Trautwein, Christian ; Luedde, Tom. / Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. In: Nature Medicine. 2019 ; Vol. 25, No. 7. pp. 1054-+.

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Kather, JN, Pearson, AT, Halama, N, Jaeger, D, Krause, J, Loosen, SH, Marx, A, Boor, P, Tacke, F, Neumann, UP , Grabsch, HI, 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, vol. 25, no. 7, pp. 1054-+. https://doi.org/10.1038/s41591-019-0462-y

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. / Kather, Jakob Nikolas; Pearson, Alexander T.; Halama, Niels; Jaeger, Dirk; Krause, Jeremias; Loosen, Sven H.; Marx, Alexander; Boor, Peter; Tacke, Frank; Neumann, Ulf Peter ; Grabsch, Heike I.; Yoshikawa, Takaki; Brenner, Hermann; Chang-Claude, Jenny; Hoffmeister, Michael; Trautwein, Christian; Luedde, Tom.

In: Nature Medicine, Vol. 25, No. 7, 07.2019, p. 1054-+.

Research output: Contribution to journal › Article › Academic › peer-review

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