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. 2022 Oct 13;12:955866. doi: 10.3389/fonc.2022.955866

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Copyright © 2022 Li, Wu, Li, Dai, Liao, Yang, Chen and Huang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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LASSO regression for radiomic feature selection in K^trans (A, B), V_e images (C, D) and K^trans +V_e images (E, F). (A, C, E) Selection of the optimal value of lambda (λ). Tuning log(λ) selection in the LASSO model used to perform 10-fold cross-validation via the minimum criteria. The y-axis indicates the partial likelihood deviance, while the lower x-axis indicates the log (λ) and the upper x-axis represents the average number of predictors. (B), (D), (F): Each colored curve represents the trajectory of the change of an independent variable. Lambda values of 0.04682979 (B), 0.00985883 (D), and 0.03542502 (F) were selected as the optimal values, respectively. LASSO, Least absolute shrinkage and selection operator.