Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer-Reference-Cited by-同舟云学术

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

Published:2023-01-09 Issue:2 Volume:29 Page:430-439
ISSN:1078-8956
Container-title:Nature Medicine
language:en
Short-container-title:Nat Med

Author:

Foersch Sebastian^ORCID,Glasner Christina,Woerl Ann-Christin,Eckstein Markus,Wagner Daniel-Christoph,Schulz Stefan,Kellers Franziska^ORCID,Fernandez Aurélie,Tserea Konstantina,Kloth Michael,Hartmann Arndt,Heintz Achim,Weichert Wilko,Roth Wilfried,Geppert Carol,Kather Jakob Nikolas^ORCID,Jesinghaus Moritz^ORCID

Funder

Bundesministerium für Bildung und Forschung

Stage-I-Program of the University Medical Center Mainz Mainz Research School of Translational Biomedicine (TransMed) Manfred-Stolte-Foundation

Bundesministerium für Gesundheit

Deutsche Krebshilfe

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Link

https://www.nature.com/articles/s41591-022-02134-1.pdf

Reference41 articles.

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2. Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

3. Pages, F. et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391, 2128–2139 (2018).

4. Nagtegaal, I. D. et al. The 2019 WHO classification of tumours of the digestive system. Histopathology 76, 182–188 (2020).

5. Argiles, G. et al. Localised colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 31, 1291–1305 (2020).

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