Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting 252Cf neutron therapy response in cervical cancer

Figure 1 Model performance comparison in the validation set. A: Receiver operating characteristic (ROC) curves: Model-Fusion (AUC = 0.892) significantly outperformed the other three models (all P < 0.05); B: Calibration curve: Model-Fusion showed good agreement with the ideal diagonal (Hosmer-Lemeshow test, P = 0.610); C: Decision curve analysis: Model-Fusion yielded the highest net benefit across a wide range of threshold probabilities (10%-85%, red shaded area). ROC: Receiver operating characteristic; AUC: Area under curve; TPR: True positive rate; FPR: False positive rate.

Figure 2 Kaplan-Meier survival curves for different fusion model prediction groups. High benefit group (n = 21, red line) vs low benefit group (n = 9, gray dashed line). A: Progression-free survival (PFS): 3-year PFS 85.7% vs 44.4% (P = 0.008); B: Overall survival (OS): 3-year OS 92.5% vs 71.4% (P = 0.003).