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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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
September 2023
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| In: |
Gastric cancer
Year: 2023, Jahrgang: 26, Heft: 5, Pages: 708-720 |
| ISSN: | 1436-3305 |
| DOI: | 10.1007/s10120-023-01398-x |
| Online-Zugang: | 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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| Verfasserangaben: | 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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| 520 | |a 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. | ||
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