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Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study: original article

The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potenti...

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Main Authors: Veldhuizen, Gregory Patrick (Author) , Röcken, Christoph (Author) , Behrens, Hans-Michael (Author) , Cifci, Didem (Author) , Muti, Hannah Sophie (Author) , Yoshikawa, Takaki (Author) , Arai, Tomio (Author) , Oshima, Takashi (Author) , Tan, Patrick (Author) , Ebert, Matthias (Author) , Pearson, Alexander T. (Author) , Calderaro, Julien (Author) , Grabsch, Heike I. (Author) , Kather, Jakob Nikolas (Author)
Format: Article (Journal)
Language:English
Published: September 2023
In: Gastric cancer
Year: 2023, Volume: 26, Issue: 5, Pages: 708-720
ISSN:1436-3305
DOI:10.1007/s10120-023-01398-x
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s10120-023-01398-x
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s10120-023-01398-x
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Author Notes:Gregory Patrick Veldhuizen, Christoph Röcken, Hans-Michael Behrens, Didem Cifci, Hannah Sophie Muti, Takaki Yoshikawa, Tomio Arai, Takashi Oshima, Patrick Tan, Matthias P. Ebert, Alexander T. Pearson, Julien Calderaro, Heike I. Grabsch, Jakob Nikolas Kather
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Summary:The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC.
Item Description:Online veröffentlicht: 3. Juni 2023
Gesehen am 26.02.2024
Physical Description:Online Resource
ISSN:1436-3305
DOI:10.1007/s10120-023-01398-x

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