Fig. 2
The selection of optimal feature subset using least absolute shrinkage and selection operator (LASSO) regression with ten-fold cross-validation. (A) The vertical axis represents the AUC value, the upper horizontal axis represents the number of radiomic features in the model, and the lower horizontal axis represents the Log(λ) value. When the tuning parameter λ and Log(λ) reach − 2.4, the AUC value of the model is maximized, selecting four radiomic features accordingly. (B) The vertical axis represents the coefficient values, the upper horizontal axis represents the number of radiomic features in the model, and the lower horizontal axis represents the Log(λ) value. As the Log(λ) value increases, the coefficients of the radiomic features gradually compress to 0.
