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. 2020 Oct 21;10:17980. doi: 10.1038/s41598-020-74599-4

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© The Author(s) 2020

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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Results from the Recursive Feature Elimination using Random Forest (RFE-RF) algorithm, used to identify key CT liver texture features to differentiate between AAH and control patients. A total of 178 texture features, and a combination of 23 of these features were associated with the best model performance (85.4% accuracy in distinguishing AAH from control patients). These 23 texture features are listed in Table 3. The 23-feature model applied to the left-out test set is described in the bottom-right table and resulted in an accuracy of 82.4% (14 of 17 patients), sensitivity of 100%, specificity of 75%, NPV of 100%, and PPV of 62.75%.