| Literature DB >> 31160815 |
Jakob Nikolas Kather1,2,3,4,5, Alexander T Pearson6, Niels Halama7,8,9, Dirk Jäger7,10,8, Jeremias Krause11, Sven H Loosen11, Alexander Marx12, Peter Boor13, Frank Tacke14, Ulf Peter Neumann15, Heike I Grabsch16,17, Takaki Yoshikawa18,19, Hermann Brenner7,20,21, Jenny Chang-Claude22,23, Michael Hoffmeister20, Christian Trautwein11, Tom Luedde24.
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.Entities:
Mesh：
Year: 2019 PMID： 31160815 PMCID： PMC7423299 DOI： 10.1038/s41591-019-0462-y
Source DB: PubMed Journal: Nat Med ISSN： 1078-8956 Impact factor: 53.440