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©The Author(s) 2024.
World J Clin Cases. Sep 16, 2024; 12(26): 5908-5921
Published online Sep 16, 2024. doi: 10.12998/wjcc.v12.i26.5908
Published online Sep 16, 2024. doi: 10.12998/wjcc.v12.i26.5908
Figure 2 Least absolute shrinkage and selection operator regression model for screening the radiomics characteristics of the training group.
A: Screening of the radiomics features was performed through least absolute shrinkage and selection operator (Lasso) regression. The cross validation for Lasso regression, where the parameter λ was adjusted to find the best function set, is shown. The vertical dotted line on the left panel represents the log(λ) corresponding to the optimal λ; B: Screening of the radiomics features was performed through Lasso regression. The coefficients of texture parameters changed with λ. The vertical line corresponds to the 10 features selected with non-zero Lasso cross-validation coefficients. MSE: Mean-square error.
- Citation: Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12(26): 5908-5921
- URL: https://www.wjgnet.com/2307-8960/full/v12/i26/5908.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i26.5908