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. 2021 Jan 11;11:395. doi: 10.1038/s41598-020-79880-0

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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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Malignant breast mass correctly classified by the nomogram. (a) A 51-year-old woman in the validation cohort had a screening US-detected 0.9-cm breast mass assigned as Breast Imaging Reporting and Data System (BI-RADS) 4A. The mass was confirmed as grade 1 invasive ductal carcinoma (pT1N0M0, estrogen receptor positive, progesterone receptor negative, human epidermal growth factor receptor type 2 negative) by US-guided 14-gauge core needle biopsy and subsequent surgery. (b) The deep learning based computer-aided diagnosis (DL-CAD) software assigned irregular shape score of 0.90 and parallel orientation score of 0.12. (c) This mass has 185 points on the nomogram, which was higher than the cut-off of 114, requiring biopsy. The nomogram incorporating quantitative morphologic scores from DL-CAD software correctly identified the true cancer.