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

Diagnosis of lung cancer through manual histopathology evaluation is insufficient to predict patient survival. Here, the authors use computerized image processing to identify diagnostically relevant image features and use these features to distinguish lung cancer patients with different prognoses.

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Autores principales:	Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin, Michael Snyder
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/412254c245364c069c2a4448a132d6dd
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spelling	oai:doaj.org-article:412254c245364c069c2a4448a132d6dd2021-12-02T15:34:52ZPredicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features10.1038/ncomms124742041-1723https://doaj.org/article/412254c245364c069c2a4448a132d6dd2016-08-01T00:00:00Zhttps://doi.org/10.1038/ncomms12474https://doaj.org/toc/2041-1723Diagnosis of lung cancer through manual histopathology evaluation is insufficient to predict patient survival. Here, the authors use computerized image processing to identify diagnostically relevant image features and use these features to distinguish lung cancer patients with different prognoses.Kun-Hsing YuCe ZhangGerald J. BerryRuss B. AltmanChristopher RéDaniel L. RubinMichael SnyderNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-10 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kun-Hsing Yu Ce Zhang Gerald J. Berry Russ B. Altman Christopher Ré Daniel L. Rubin Michael Snyder Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
description	Diagnosis of lung cancer through manual histopathology evaluation is insufficient to predict patient survival. Here, the authors use computerized image processing to identify diagnostically relevant image features and use these features to distinguish lung cancer patients with different prognoses.
format	article
author	Kun-Hsing Yu Ce Zhang Gerald J. Berry Russ B. Altman Christopher Ré Daniel L. Rubin Michael Snyder
author_facet	Kun-Hsing Yu Ce Zhang Gerald J. Berry Russ B. Altman Christopher Ré Daniel L. Rubin Michael Snyder
author_sort	Kun-Hsing Yu
title	Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
title_short	Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
title_full	Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
title_fullStr	Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
title_full_unstemmed	Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
title_sort	predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/412254c245364c069c2a4448a132d6dd
work_keys_str_mv	AT kunhsingyu predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT cezhang predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT geraldjberry predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT russbaltman predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT christopherre predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT daniellrubin predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures AT michaelsnyder predictingnonsmallcelllungcancerprognosisbyfullyautomatedmicroscopicpathologyimagefeatures
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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

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