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. 2021 Mar 20;21:54. doi: 10.1186/s12880-021-00581-9

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© The Author(s) 2021

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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — a–f LASSO regression was used for feature selection. The deviance curve was plotted, and the parameter (λ) selection was tuned using tenfold cross-validation. Dotted lines denote the minimum criterion (right) and 1-SE of the minimum criteria (left). The 1-SE criterion was applied, and there were respectively 7 features in the T1WI sequence (a, b) with non-zero coefficients (the optimal value of λ = 0.07543); 8 features of the T2WI sequence (c, d) with non-zero coefficients (the optimal value of λ = 0.03457); 8 of the T1-2WI sequence (e, f) with non-zero coefficients (the optimal value of λ = 0.06485). g–i Spearman’s correlation coefficients were calculated for the features in the T1WI, T2WI and T1-2WI sequences. No pair of features showed extremely strong positive correlations among the feature groups (0.90–1.00)