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. 2026 Oct 14;16:1942820. doi: 10.3389/fonc.2026.1942820

Figure 4.

Panel of nine scientific data visualizations related to statistical model development and validation for survival analysis; includes plots for partial likelihood deviance versus log lambda (a), coefficient trajectories (b), hazard ratios with confidence intervals for three variables (c), a nomogram for estimating survival probability based on selected variables (d), two ROC curves showing sensitivity versus 1-specificity with AUCs for different time points (e, f), a Kaplan-Meier survival curve comparing risk groups (g), a calibration curve comparing observed versus predicted survival probabilities at 365 days (h), and a decision curve analysis showing net benefit across threshold probabilities (i).

Development and internal validation of an Elastic Net-Cox prognostic signature for mCRPC. (a) Ten-fold cross-validation profile of partial-likelihood deviance plotted against log(λ); vertical dashed lines denote λ.1se (green) and λ.min (yellow). (b) Regularization paths illustrating the shrinkage of individual coefficients as the penalty parameter increases. (c) Forest plot of hazard ratios and 95% confidence intervals from the multivariable Cox model incorporating Elastic Net-selected variables. (d) A three-predictor nomogram for estimating mCRPC-free survival probabilities at 365 and 545 days; individual predictor scores are summed to yield the total risk score and corresponding survival estimate. (e, f) Time-dependent receiver operating characteristic (ROC) curves assessing discriminative performance in the training cohort (e) and the validation cohort, respectively. (g) Kaplan-Meier survival estimates. (h) Calibration plots comparing predicted and observed mCRPC-free survival probabilities across both cohorts. (i) DCA evaluating the net clinical benefit.