Fig. 2.

LASSO Cox regression analyses of three radiomics models. A, C, E The partial likelihood deviance was plotted versus log (λ). The y-axis indicates the partial likelihood deviance, while the lower x-axis indicates the log (λ) and the upper x-axis represents the average number of predictors. Dotted vertical lines were drawn at the optimal values using the minimum criteria and 1 standard error of the minimum criteria. The tuning parameter (λ) was selected in the LASSO model via 10-fold cross-validation based on minimum criteria. A The lambda.min used in the LASSO algorithm for the periprostatic fat radiomics score is 0.05181153; B The lambda.min used in the LASSO algorithm for the intratumoral radiomics score is 0.08555169; C The lambda.min used in the LASSO algorithm for the periprostatic fat-intratumoral radiomics score is 0.07075123. The coefficients (y-axis) were plotted against log (lambda) and (B) 8 features with nonzero coefficients were selected to build periprostatic fat radiomics model, D 6 features with nonzero coefficients were selected to build intratumoral radiomics model, F and 8 features with nonzero coefficients were selected to build periprostatic fat-intratumoral radiomics model. LASSO, the Least absolute shrinkage and selection operator