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Title:

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Foersch, Sebastian; Glasner, Christina; Woerl, Ann-Christin; Eckstein, Markus; Wagner, Daniel-Christoph; Schulz, Stefan; Kellers, Franziska; Fernandez, Aurélie; Tserea, Konstantina; Kloth, Michael; Hartmann, Arndt; Heintz, Achim; Weichert, Wilko; Roth, Wilfried; Geppert, Carol; Kather, Jakob Nikolas; Jesinghaus, Moritz
Abstract:
Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 pati...     »
Journal title abbreviation:
Nat Med
Year:
2023
Journal volume:
29
Journal issue:
2
Pages contribution:
430-439
Fulltext / DOI:
doi:10.1038/s41591-022-02134-1
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36624314
Print-ISSN:
1078-8956
TUM Institution:
Institut für Allgemeine Pathologie und Pathologische Anatomie (Dr. Mogler komm.)
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