Details

Autor(en) / Beteiligte

• 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
• Luedde, Tom

Titel

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

Ist Teil von

• Nature medicine, 2019-07, Vol.25 (7), p.1054-1056

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2019

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.