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. 2021 Jun 3;11:11698. doi: 10.1038/s41598-021-91278-0

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Performances of the 12 best circulating microRNA signatures able to discriminate breast cancers from healthy women in Rwandan-recruited subjects. (A) ROC curve analysis, optimal cut-off, and corresponding specificity and sensibility (red brackets) of the top signatures. Optimal cut-offs are calculated using the Youden index and are displayed in red. The confidence intervals of the AUC values are displayed in black brackets (B) Model outcome distributions for the breast cancer and healthy subjects. The x-axis corresponds to the model predictions. The dashed line represents the chosen threshold used to compute the sensitivity and specificity values for each group. Orange distribution contains healthy subjects; breast cancer subject distributions are displayed in blue. Names of the signatures are referring to Table 3.