Fig. 4

Feature selection based on least absolute shrinkage and selection operator (LASSO) regression. a Forty-five features (14 histogram features and 31 texture features) with p < 0.05 selected by univariable analysis. b The trend graph of the mean square error (MSE) with different λ (Lamda) during cross-validation. λ is an important parameter of LASSO regression that is usually adjusted by cross-validation to find the optimal value. The red dots represent the average values of the MSE. The blue error bars represent the standard deviation of the MSE. The black dotted line indicates the best value of λ. c The convergence graph of the weight coefficients of the features under different λ values. Each convergence line corresponds to a feature, and the color of the line matches the color before the feature name in a. As shown in b and c, the MSE is minimized (0.21 ± 0.07) at λ = 0.044 (the black dotted line), where five representative features were finally identified (weight coefficient ≠ 0). d Feature names and weight coefficients of the five selected features. GLCM gray level co-occurrence matrix, GLDM gray level dependence matrix, GLRLM gray level run length matrix, GLSZM gray level size zone matrix