Fig. 2

LASSO regression-based selection of deep learning radiomics features. The optimal λ value of 0.012 was selected. And performance of the machine learning model based on the AdaBoost algorithm. a Feature coefficients corresponding to the value of parameter λ. Each line represents the change trajectory of each independent variable. b The most valuable features were screened out by tuning λ using LASSO regression. The dotted vertical line represents the optimal log(λ) value determined through cross-validation. c Feature importance ranking based on the LASSO-selected radiomic features using AdaBoost. The y-axis indicates the selected deep learning radiomics features, and the x-axis represents their relative importance. d ROC curve. e Calibration curve. f Decision curve analysis