Interpretable survival prediction for colorectal cancer using deep learning
Interpretable survival prediction for colorectal cancer using deep learning
About this item
Full title
Author / Creator
Wulczyn, Ellery , Steiner, David F. , Moran, Melissa , Plass, Markus , Reihs, Robert , Tan, Fraser , Flament-Auvigne, Isabelle , Brown, Trissia , Regitnig, Peter , Chen, Po-Hsuan Cameron , Hegde, Narayan , Sadhwani, Apaar , MacDonald, Robert , Ayalew, Benny , Corrado, Greg S. , Peng, Lily H. , Tse, Daniel , Müller, Heimo , Xu, Zhaoyang , Liu, Yun , Stumpe, Martin C. , Zatloukal, Kurt and Mermel, Craig H.
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
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More information
Scope and Contents
Contents
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239...
Alternative Titles
Full title
Interpretable survival prediction for colorectal cancer using deep learning
Authors, Artists and Contributors
Author / Creator
Steiner, David F.
Moran, Melissa
Plass, Markus
Reihs, Robert
Tan, Fraser
Flament-Auvigne, Isabelle
Brown, Trissia
Regitnig, Peter
Chen, Po-Hsuan Cameron
Hegde, Narayan
Sadhwani, Apaar
MacDonald, Robert
Ayalew, Benny
Corrado, Greg S.
Peng, Lily H.
Tse, Daniel
Müller, Heimo
Xu, Zhaoyang
Liu, Yun
Stumpe, Martin C.
Zatloukal, Kurt
Mermel, Craig H.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_da537cd907ad48cb854376055689af37
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_da537cd907ad48cb854376055689af37
Other Identifiers
ISSN
2398-6352
E-ISSN
2398-6352
DOI
10.1038/s41746-021-00427-2
