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

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 routi...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	The Lancet. Digital health 2021-10, Vol.3 (10), p.e654-e664
Hauptverfasser:	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
Format:	Artikel
Sprache:	eng
Schlagworte:	Aged Cohort Studies Deep Learning Epstein-Barr Virus Infections - complications Epstein-Barr Virus Infections - diagnosis Female Germany Histological Techniques - methods Humans Italy Japan Male Microsatellite Instability Middle Aged Reproducibility of Results Republic of Korea Retrospective Studies Stomach Neoplasms - complications Stomach Neoplasms - genetics Switzerland United Kingdom United States
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!