ArticleWorld journal of otorhinolaryngology - head and neck surgery2026
Multi-Sequence Fusion MRI Deep Learning Models for Discrimination of Recurrent Nasopharyngeal Carcinoma and Osteoradionecrosis.
Article in World journal of otorhinolaryngology - head and neck surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( 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, Conclusion: The proposed AI model effectively integrates information from multiple MRI sequences, significantly improving classification performance in distinguishing rNPC from ORN.
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