Multi-Sequence Fusion MRI Deep Learning Models for Discrimination of Recurrent Nasopharyngeal Carcinoma and Osteoradionecrosis.

Abstract

Objectives: To develop a multi-sequence fusion model based on MRI images to differentiate between recurrent nasopharyngeal carcinoma (rNPC) and osteoradionecrosis (ORN) in patients with nasopharyngeal carcinoma (NPC) after radiotherapy, and to evaluate its diagnostic performance.

Methods: We retrospectively reviewed 370 patients with pathologically confirmed recurrent nasopharyngeal carcinoma (rNPC) or osteoradionecrosis (ORN). After screening, 346 patients (207 rNPC and 139 ORN) with both axial T1-weighted contrast-enhanced imaging (T1C) and T2-weighted imaging (T2WI) were included and randomly split into a training set (n = 212), validation set (n = 61), and test set (n = 73). A dual-branch 3D-ResNet-50-based fusion model was developed to integrate multi-sequence MRI features. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, precision, and F1 score.

Results: On the test set, the proposed 3D-ResNet-Fusion model achieved an AUC of 0.86 and an accuracy of 84%, with a sensitivity of 91%, specificity of 72%, precision of 83%, and an F1 score of 0.87. Based on AUC comparisons, the fusion model outperformed comparator models based on EfficientNet (0.77, p = 0.20) and DenseNet (0.70, p = 0.02), as well as single-sequence 3D-ResNet models trained on T1C alone (0.71, p = 0.02) or T2WI alone (0.73, p = 0.046). In the human-AI comparison experiment, the proposed AI model achieved a 3% higher ACC than the senior otolaryngologist (p = 0.83), and outperformed the two junior otolaryngologists by 15% (p = 0.05) and 22% (p < 0.01), respectively.

Conclusion: The proposed AI model effectively integrates information from multiple MRI sequences, significantly improving classification performance in distinguishing rNPC from ORN.