Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer

Figure 2 Radiomics feature selection and coefficient visualization using LASSO regression. A and B: The optimal regularization parameter (λ) for the LASSO regression model was selected via 10-fold cross-validation in the training (A) and test (B) cohorts. The plots display binomial deviance against log(λ), with the vertical dashed line indicating the optimal log(λ) of -4. This resulted in 6 non-zero features for the training cohort and 9 for the test cohort; C and D: The corresponding coefficients of the selected radiomics features are shown for the training (C) and test (D) cohorts. The Y-axis lists the selected features, and the X-axis shows their respective coefficients.