Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection

Figure 3 The calibration and discrimination performance of deep learning-based radiomics score for event-free survival in hepatoblastoma patients receiving surgical resection. A and B: Calibration curves for deep learning-based radiomics (DLBR) score, the clinical model, and the integrated nomogram model yielding agreement degrees between predicted and observational survival probabilities of event-free survival (EFS) for patients in the training (left) and testing (right) databases at the time of 36 months (A) and 60 months (B). The gray line of y = x represents a perfect predictive power by an ideal model. The fit goodness with this diagonal line coincided with the model’s predictive performance; C: Time-dependent Harrell’s C-indexes for DLBR score, the clinical model, and the integrated nomogram model on EFS for the training (left) and testing (right) cohorts; D: Time-dependent Brier scores in estimation of prediction errors for DLBR score, the clinical model, and the integrated nomogram model on EFS for the training (left) and testing (right) cohorts. DLBR: Deep learning-based radiomics; AUC: Area under the receiver operating characteristic curve.