Clinical trial · Observational
Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images
Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
NCT05627310CI-TRIAL-00062340unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Nasopharyngeal Carcinoma | Nasopharyngeal Carcinoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Diagnostic | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- Training Cohort
- description
- Nasopharyngeal endoscopic images collected from 8 hospitals all over China
- label
- Validation Cohort
- description
- Nasopharyngeal endoscopic images collected from 8 hospitals all over China
- interventionNames
- Other: Diagnostic
- label
- Testing Cohort
- description
- Nasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China
- interventionNames
- Other: Diagnostic
Primary outcomes (1)
- measure
- Area under the receiver operating characteristic curve of the deep learning algorithm
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * The quality of endoscopic images should clinical acceptable. * Patients were diagnosed with biopsy(NPC, benign hyperplasia). Control corhort(normal nasopharynx) don't require bispsy result. Exclusion Criteria: * images with spots from lens flares or stains, and overexposure were excluded from further analysis. * image can not expose most part of lesion clearly.
References
Publications (1)
- DERIVEDWang W, Jin Z, Liu X, Chen X. NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images. Comput Med Imaging Graph. 2025 Jun;122:102524. doi: 10.1016/j.compmedimag.2025.102524. Epub 2025 Mar 12. PMID 40088572