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. 2020 Sep 9;31(3):1460–1470. doi: 10.1007/s00330-020-07174-0

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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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Fig. 5 — Principal component analysis (PCA) of the brain and liver material radiomics distribution before and after convolution kernel–slice thickness ComBat correction. The distance between the radiomics data of the brain and liver materials from CT scans with different acquisitions protocols increases after applying batch correction (i.e., the radiomics distribution better reflects differences between materials and not due to the CT-acquisition parameters)