Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

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Tytuł:: Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study
Autorzy:: Muti, Hannah Sophie
Heij, Lara Rosaline
Keller, Gisela
Kohlruss, Meike
Langer, Rupert
Dislich, Bastian
Cheong, Jae-Ho
Kim, Young-Woo
Kim, Hyunki
Kook, Myeong-Cherl
Cunningham, David
Allum, William H
Langley, Ruth E
Nankivell, Matthew G
Quirke, Philip
Hayden, Jeremy D
West, Nicholas P
Irvine, Andrew J
Yoshikawa, Takaki
Oshima, Takashi
Huss, Ralf
Grosser, Bianca
Roviello, Franco
d'Ignazio, Alessia
Quaas, Alexander
Alakus, Hakan
Tan, Xiuxiang
Pearson, Alexander T
Luedde, Tom
Ebert, Matthias P
Jäger, Dirk
Trautwein, Christian
Gaisa, Nadine Therese
Grabsch, Heike I
Kather, Jakob Nikolas
Źródło:: The Lancet Digital Health; 20210101, Issue: Preprints
: Periodyk

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Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides.

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