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. 2019 Aug 29;9:12529. doi: 10.1038/s41598-019-48738-5

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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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Importance of features in tumor classification. (a) Feature importance was estimated for the random forest model using the entire tumor volume and a GLCM of size 512. The top 10 most important features included both 1st and 2nd order texture features. (b) Cluster prominence derived from a GLCM of size 512 was calculated for each tumor type. Cluster prominence was significantly difference between medulloblastoma and glioma, both U87 and GL261 tumors. Box plot represents 25th and 75th percentiles; bar = min and max values; line = median, ***p < 0.0002, ****p < 0.0001.