Interpretable survival prediction for colorectal cancer using deep learning

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 ev...

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Detalles Bibliográficos
Autores principales: 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, Mermel, Craig H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055695/
https://www.ncbi.nlm.nih.gov/pubmed/33875798
http://dx.doi.org/10.1038/s41746-021-00427-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055695/
https://www.ncbi.nlm.nih.gov/pubmed/33875798
http://dx.doi.org/10.1038/s41746-021-00427-2

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