ArticleiScience2024
Advancing precise diagnosis of nasopharyngeal carcinoma through endoscopy-based radiomics analysis.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study.Annals of medicine · 2026Article
- Application of artificial intelligence in early warning, diagnosis and precise treatment of head and neck tumors (Review).Molecular and clinical oncology · 2026Review
- Deep learning-radiomics-SUVmax integration fromFrontiers in oncology · 2026Article
- Immunotherapy for locoregionally advanced nasopharyngeal carcinoma: a meta-analysis of randomized clinical trials.Therapeutic advances in medical oncology · 2026Article
- Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study.Frontiers in oncology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.