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. 2020 Jan 3;11:1. doi: 10.1186/s13244-019-0795-6

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Fig. 2 — The best-fit linear regression model allows imaging features to differentiate tumors with different Oncotype Dx Recurrence Score (ODxRS). a Sagittal T1-weighted fat-suppressed post-contrast MRI of an invasive ductal nuclear grade 1 carcinoma with an ODxRS of 10 and (b) corresponding kurtosis histogram, which demonstrates the frequency of MR intensity. c Sagittal T1-weighted fat-suppressed postcontrast MRI of an invasive ductal nuclear grade 2 carcinoma with an ODxRS of 21 and (d) corresponding kurtosis histogram. e Sagittal T1-weighted fat-suppressed postcontrast MRI of an invasive ductal nuclear grade 3 carcinoma with an ODxRS of 43 and (f) corresponding kurtosis histogram. Reprinted with permission from: J Magn Reson Imaging. 2015 Nov;42 [5]:1398–406. doi: 10.1002/jmri.24890 Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB, Deasy JO, Morris EA.