Fig. 3

Feature selection via the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. aThe LASSO regression method was utilized to select radiomic features. A 10-fold cross-validation method was utilized to screen hyperparameter (λ) of the LASSO regression model and choose the model with the smallest error (λ), b LASSO coefficient profiles of the features represent vertical lines that are drawn at the value selected via 10-fold cross-validation, and the optimized hyperparameter λ was determined to be 0.00677, and 7 radiomic features were remained. c By LASSO logistic regression analysis, 7 optimal radiomic features were identified for reconstructing the prediction model