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. 2019 Feb 26;9:2764. doi: 10.1038/s41598-019-39206-1

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© The Author(s) 2019

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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Normalized value of three radiomic features of interest across 64 convolutional filters within the final convolutional layer. x-axis represents which filter within the third convolutional block. y-axis represents the value of the radiomic feature, normalized across all filters. Blue bars represent ZSV_GLSZM, red bars represent ZSN_GLSZM and yellow bars represent LRHGE_GLRLM. Of particular interest are the numerous blue peaks (a, c and d). These filters are strongly activated by an input region with a high value of the radiomic feature ZSV_GLSZM, precisely the feature that Vallières et al.²⁵ found to be most predictive. Many of these filters are also strongly activated by extreme (high or low) values of ZSN_GLSZM and LRHGE_GLRLM (red and yellow respectively). In particular, many filters represent various permutations of the three features. As an example, (a) is activated by all three radiomic features, (b) is mostly activated by red, (c) is mostly activated by blue and yellow, while (d) is mostly activated by blue. The lettering scheme corresponds to the maximal activation maps shown in Fig. 3.