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. 2022 Sep 7;12:15142. doi: 10.1038/s41598-022-19084-w

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© The Author(s) 2022, corrected publication 2022

Open AccessThis 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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Sensitivity contributions of factors investigated in final radiomics metrics results. First-order statistics are mostly sensitive to variations in the reconstruction type, whereas texture features can be seen to be mostly sensitive to grey level binning variations. Two features, Skewness and Kurtosis, can be seen to be robust to these changes and mostly sensitive to variations in the tumour model. The feature IDs (x-axis) follows Supplementary Tables ST1 and ST2.