A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

Background Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucid...

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Veröffentlicht in:	Breast cancer research : BCR Jg. 22; H. 1; S. 12 - 10
Hauptverfasser:	Jaber, Mustafa I., Song, Bing, Taylor, Clive, Vaske, Charles J., Benz, Stephen C., Rabizadeh, Shahrooz, Soon-Shiong, Patrick, Szeto, Christopher W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 28.01.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Biomedical and Life Sciences Biomedicine Biopsy Breast cancer Breast tumors Cancer Cancer Research Classification Data mining Datasets Decision making Deep learning Deep learning algorithm Development and progression ErbB-2 protein Gene expression Health aspects Hormones Image processing Intrinsic molecular subtype (IMS) Learning algorithms Machine learning Medical prognosis Methods Oncology Patients Principal components analysis Prognosis Research Article Ribonucleic acid RNA RNA sequencing Surgical Oncology Survival Tumors Whole-slide imaging (WSI) Breast cancer Intrinsic molecular subtype (IMS) Whole-slide imaging (WSI) Deep learning algorithm
ISSN:	1465-542X, 1465-5411, 1465-542X
Online-Zugang:	Volltext
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