Advancing precise diagnosis of nasopharyngeal carcinoma through endoscopy-based radiomics analysis

Nasopharyngeal carcinoma (NPC) has high metastatic potential and is hard to detect early. This study aims to develop a deep learning model for NPC diagnosis using optical imagery. From April 2008 to May 2021, we analyzed 12,087 nasopharyngeal endoscopic images and 309 videos from 1,108 patients. The pretrained model was fine-tuned with stochastic gradient descent on the final layers. Data augmentation was applied during training. Videos were converted to images for malignancy scoring. Performance metrics like AUC, accuracy, and sensitivity were calculated based on the malignancy score. The deep learning model demonstrated high performance in identifying NPC, with AUC values of 0.981 (95% confidence of interval [CI] 0.965-0.996) for the Fujian Cancer Hospital dataset and 0.937 (0.905-0.970) for the Jiangxi Cancer Hospital dataset. The proposed model effectively diagnoses NPC with high accuracy, sensitivity, and specificity across multiple datasets. It shows promise for early NPC detection, especially in identifying latent lesions.