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. 2018 Aug 6;8:11773. doi: 10.1038/s41598-018-30273-4

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© The Author(s) 2018

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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IDH mutation predictive modeling with and without lesion location information radiomics. (A) Overall diagnostic accuracy for the training set and the validation set is shown. Diagnostic accuracy without lesion location information was as high as 0.82 for the training set and 0.83 for the validation set. This accuracy further improved to 0.85 (p = 0.01) and 0.87 (p = 0.04) respectively by including lesion location info. Sensitivity and negative predictive value also improved significantly by implementing lesion location information in predictive modeling (*p < 0.05). (B) Radiomic components significant for predictive modeling is shown. Frontal lobe tumor involvement (MNI_str_loc.04) was one of the most significant features for being the tumor to be IDH mutated, while the magnitude of contrast enhancement (Gdzscore_ara.of.Gd.) was for IDH-wt. Averages and standard deviations of 5 repetitive analyses are shown for both (A) and (B). PPV stands for “positive predictive value” and NPP for “negative predictive value”.