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Titel:

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

Dokumenttyp:
Journal Article
Autor(en):
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,...     »
Abstract:
BACKGROUND: 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. METHODS: In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Swit...     »
Zeitschriftentitel:
Lancet Digit. Health
Jahr:
2021
Band / Volume:
3
Heft / Issue:
10
Seitenangaben Beitrag:
e654-e664
Volltext / DOI:
doi:10.1016/S2589-7500(21)00133-3
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/34417147
TUM Einrichtung:
Institut für Allgemeine Pathologie und Pathologische Anatomie
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