Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer

Figure 3 Texture feature selection using the Least Absolute Shrinkage and Selection Operator binary logistic regression model. A: The tuning parameter (Lambda) selection in the Least Absolute Shrinkage and Selection Operator (LASSO) model was performed using 10-fold cross-validation with the minimum criterion. The relationship curve between the mean-square error and Lambda is depicted, with a dashed line indicating the optimal value. The vertical lines represent the values selected through 10-fold cross-validation, including 18 optimized non-zero coefficients; B: LASSO coefficient profiles of 1169 texture features. The coefficient profiles were generated based on the sequence of log (Lambda). When using the value selected by 10-fold cross-validation, the optimal Lambda resulted in 18 non-zero coefficients. MSE: Mean-square error.