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Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI

Crossref DOI link: https://doi.org/10.1007/s00330-021-08146-8

Published Online: 2021-07-05

Published Print: 2022-01

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lee, Ji Young

Lee, Kwang-sig

Seo, Bo Kyoung https://orcid.org/0000-0002-9512-5361
Cho, Kyu Ran

Woo, Ok Hee

Song, Sung Eun

Kim, Eun-Kyung

Lee, Hye Yoon

Kim, Jung Sun

Cha, Jaehyung
Funding

Funding for this research was provided by:

Ministry of Science and ICT (MSIT) of Korea (NRF-2021R1A2C1010565, IITP-2018-0-01405)

Korea University (K2011241)

Dongkook Life Science Co.,Ltd. (NA)
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Text and Data Mining valid from 2021-07-05

Version of Record valid from 2021-07-05
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Article History

Received: 27 January 2021

Revised: 1 June 2021

Accepted: 9 June 2021

First Online: 5 July 2021

Declarations

:

: The scientific guarantor of this publication is Bo Kyoung Seo.

: The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

: Two of the authors has significant statistical expertise.

: Written informed consent was obtained from all subjects (patients) in this study.

: Institutional Review Board approval was obtained.

: • Prospective• Diagnostic or prognostic study• Performed at one institution

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