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. 2017 Dec 20;7:17946. doi: 10.1038/s41598-017-17858-1

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© The Author(s) 2017

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Identification of informative nuclear morphometric features for cancer prognosis. (a) Whisker box plot for HC/EC of normal and cancer cell lines (BJ: n = 134, HMEC: n = 101, MCF10A: n = 156, MCF7: n = 112; Student’s unpaired t-test ***p < 0.0001, **p < 0.0001 and ns = not significant). (b) Analysis of texture features via extraction of nuclear patches of size 32 × 32 pixels with stride size of 16 pixels in either direction. (c) Table indicating differences in accuracy between patch-based classification and classification based on full nuclear images for both neural and linear models.