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. 2021 Jul 7;11:14057. doi: 10.1038/s41598-021-92072-8

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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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Top Radiomic Features identified by the Random Survival Forest (RSF) for Overall Survival (OS). Boxplots of top 6 features selected using the variable importance from the Random Survival Forest (RSF) over the training data and their distribution within the two clusters identified for Overall Survival (OS). The difference in distribution suggests that these variables can be used in a model to assign cluster labels to test patients. Radiomic features names have been abbreviated to fit in the figure: GL = GrayLevel, CoM = CoocurrenceMatrix, RL = RunLength.