Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning

[Display omitted] HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting individual prognosis is of great significance for the further development of precise therapy. With the c...

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Veröffentlicht in:	Computational and structural biotechnology journal Jg. 20; S. 333 - 342
Hauptverfasser:	Yang, Jialiang, Ju, Jie, Guo, Lei, Ji, Binbin, Shi, Shufang, Yang, Zixuan, Gao, Songlin, Yuan, Xu, Tian, Geng, Liang, Yuebin, Yuan, Peng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.01.2022 Research Network of Computational and Structural Biotechnology Elsevier
Schlagworte:	biotechnology Breast cancer breast neoplasms cancer recurrence computers Convolutional neural network eosin genome H&E-stained histological images HER2 histopathology hospitals metastasis neural networks patients prediction prognosis Recurrence risk risk assessment surgery Recurrence H&E-stained histological images Breast cancer HER2 Convolutional neural network
ISSN:	2001-0370, 2001-0370
Online-Zugang:	Volltext
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