A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer

Figure 2

LASSO selection and the predictive efficacy of radiomics features. (a). Tuning parameter (λ) selection with minimum criteria-based 10-fold cross-validation in the LASSO model. Binomial deviances (y-axis) were plotted as a function of log (λ) (lower x-axis), and the upper x-axis represents the average number of predictors. The dotted vertical lines were drawn at the optimal values of λ and the value that gave the minimum average binomial deviance was used to select radiomics features. The optimal λ value of 0.01 (log (λ) = −4.610) was selected. (b) LASSO coefficient profiles of the 136 texture features. Each colored curve represents the trajectory of the change of an independent variable. At the value selected using 10-fold cross-validation, the optimal λ resulted in fourteen coefficients.

(a)

(b)