Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutio...

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Veröffentlicht in:	PLoS medicine Jg. 16; H. 1; S. e1002730
Hauptverfasser:	Kather, Jakob Nikolas, Krisam, Johannes, Charoentong, Pornpimol, Luedde, Tom, Herpel, Esther, Weis, Cleo-Aron, Gaiser, Timo, Marx, Alexander, Valous, Nektarios A., Ferber, Dyke, Jansen, Lina, Reyes-Aldasoro, Constantino Carlos, Zörnig, Inka, Jäger, Dirk, Brenner, Hermann, Chang-Claude, Jenny, Hoffmeister, Michael, Halama, Niels
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 24.01.2019 Public Library of Science (PLoS)
Schlagworte:	Aging Artificial intelligence Artificial neural networks Bioindicators Biological markers Biology and Life Sciences Biomarkers Cancer Cancer patients Cancer research Classification Colon - pathology Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnosis Colorectal Neoplasms - mortality Colorectal Neoplasms - pathology Coloring Agents Computer and Information Sciences Consortia Deep Learning Eosine Yellowish-(YS) Epidemiology Female Fibroblasts Funding Gastroenterology Gene expression Genes Genomes Genomics Hematoxylin Hepatology Histology Hospitals Humans Image Interpretation, Computer-Assisted - methods Immunotherapy Internal medicine Male Medical care Medical imaging equipment Medical prognosis Medical research Medicine Medicine and Health Sciences Neural networks Neurons Oncology Pathology Patient outcomes Pattern recognition People and Places Prognosis Rectum - pathology Research centers Retrospective Studies Software patches Statistical models Stroma Supervision Survival Systematic review Tissues Transfer learning Tumors Aachen Germany Germany
ISSN:	1549-1676, 1549-1277, 1549-1676
Online-Zugang:	Volltext
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