Deep learning can predict microsatellite instability directly from histology in gastrointestinal can...
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
About this item
Full title
Author / Creator
Kather, Jakob Nikolas , Pearson, Alexander T. , Halama, Niels , Jäger, 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 and Luedde, Tom
Publisher
New York: Nature Publishing Group US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Nature Publishing Group US
Subjects
More information
Scope and Contents
Contents
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 histolo...
Alternative Titles
Full title
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7423299
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7423299
Other Identifiers
ISSN
1078-8956,1546-170X
E-ISSN
1546-170X
DOI
10.1038/s41591-019-0462-y
