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A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk

Klimov, Sergey; Miligy, Islam M; Gertych, Arkadiusz; Jiang, Yi; Toss, Michael S; Rida, Padmashree; Ellis, Ian O; Green, Andrew; Krishnamurti, Uma; Rakha, Emad A; Aneja, Ritu

Authors

Sergey Klimov

Arkadiusz Gertych

Yi Jiang

Padmashree Rida

Andrew Green

Uma Krishnamurti

Emad A Rakha

Ritu Aneja raneja@gsu.edu

Journal Article Type Article
Publication Date 2019-12
Journal Breast Cancer Research
Print ISSN 1465-5411
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 21
Issue 1
Article Number 83
Institution Citation Klimov, S., Miligy, I. M., Gertych, A., Jiang, Y., Toss, M. S., Rida, P., …Aneja, R. (2019). A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Research, 21(1), doi:10.1186/s13058-019-1165-5
DOI https://doi.org/10.1186/s13058-019-1165-5
Keywords Cancer Research; Oncology
Publisher URL https://doi.org/10.1186/s13058-019-1165-5
Additional Information Received: 17 January 2019; Accepted: 25 June 2019; First Online: 29 July 2019; : This work obtained ethics approval from the North West – Greater Manchester Central Research Ethics Committee under the title; Nottingham Health Science Biobank (NHSB), reference number 15/NW/0685.; : Not applicable; : The authors declare that they have no competing interests.

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