Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whol...

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Detalles Bibliográficos
Autores principales: Yu, Kun-Hsing, Zhang, Ce, Berry, Gerald J., Altman, Russ B., Ré, Christopher, Rubin, Daniel L., Snyder, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990706/
https://www.ncbi.nlm.nih.gov/pubmed/27527408
http://dx.doi.org/10.1038/ncomms12474

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990706/
https://www.ncbi.nlm.nih.gov/pubmed/27527408
http://dx.doi.org/10.1038/ncomms12474

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