Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading...

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Detalles Bibliográficos
Autores principales: Han, Wenchao, Johnson, Carol, Gaed, Mena, Gómez, José A., Moussa, Madeleine, Chin, Joseph L., Pautler, Stephen, Bauman, Glenn S., Ward, Aaron D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303108/
https://www.ncbi.nlm.nih.gov/pubmed/32555410
http://dx.doi.org/10.1038/s41598-020-66849-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303108/
https://www.ncbi.nlm.nih.gov/pubmed/32555410
http://dx.doi.org/10.1038/s41598-020-66849-2

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