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. 2021 Feb 10;11:3499. doi: 10.1038/s41598-021-82760-w

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© The Author(s) 2021

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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Radiomics workflow. The upper panel illustrates the steps taken from manual segmentation of the carotid CTA images to create ROIs for single-slice analysis (and VOIs for multi-slice analysis) to segmentation mask perturbations, prior image normalisation or resegmentation and image quantisation. The lower panel outlines the subsequent process of radiomic features extraction, robustness analysis and machine learning for the differentiation of culprit versus non-culprit carotid arteries. The predictive ability of the classifiers was assessed via five-fold stratified cross-validation.