Fig. 3

Radiomics feature selection using the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The 10-fold cross-validation (CV) process was repeated 40 times to generate the optimal penalization coefficient lambda (λ) in the LASSO model. The value of λ that gave the minimum average binomial deviance was used to select features. Dotted vertical lines were drawn at the optimal values using the minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria). A λ value of 0.0203 was chosen (the 1-SE criteria) according to 10-fold CV, where optimal λ resulted in six nonzero coefficients