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. 2020 Nov 25;10:20537. doi: 10.1038/s41598-020-76432-4

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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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Improving risk stratification in patients with takotsubo syndrome. Cardiac magnetic resonance imaging-derived texture analysis features, identified through machine learning algorithms, have a prognostic value in patients with takotsubo syndrome. Thus, these features might serve as novel imaging biomarkers for risk stratification in takotsubo syndrome.