Fig. 4

Performance of pre-NACI, post-NACI, and fusion models in training and validation sets. A, B Spearman correlation heatmaps of radiomics features for the pre-NACI and post-NACI models reveal no strong correlations among features, indicating that these features are complementary. C, D Receiver Operating Characteristic and Precision-Recall Curves for the training set show that the fusion model achieves the highest AUC, outperforming the pre-NACI and post-NACI models. E Decision curve analysis for the training set highlights the fusion model's superior net benefit across a wide range of risk thresholds. F, G Receiver Operating Characteristic and Precision-Recall Curves for the validation set demonstrate superior performance of the fusion model compared to the pre-NACI and post-NACI models. H Decision curve analysis for the validation set confirms the fusion model's consistent net benefit, showcasing its clinical utility in predicting tumor response to treatment