Deep learning in cancer pathology: a new generation of clinical biomarkers

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of rou...

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Veröffentlicht in:	British journal of cancer Jg. 124; H. 4; S. 686 - 696
Hauptverfasser:	Echle, Amelie, Rindtorff, Niklas Timon, Brinker, Titus Josef, Luedde, Tom, Pearson, Alexander Thomas, Kather, Jakob Nikolas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 16.02.2021 Nature Publishing Group
Schlagworte:	631/67/1059/602 631/67/1857 639/705/1042 692/4028/67/2321 Artificial intelligence Biomarkers Biomarkers, Tumor - analysis Biomedical and Life Sciences Biomedicine Cancer Cancer Research Clinical decision making Decision Making Deep Learning Drug Resistance Epidemiology Histology Humans Image processing Image Processing, Computer-Assisted - methods Molecular Medicine Neoplasms - pathology Neoplasms - therapy Oncology Predictions Prognosis Review Review Article Tumors
ISSN:	0007-0920, 1532-1827, 1532-1827
Online-Zugang:	Volltext
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