Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images

Eye & ENT Hospital of Fudan University

Status: Enrolling

Conditions: Nasopharyngeal Carcinoma

Treatments: Other: Diagnostic

Study type: Observational

Funder types: Other

Identifiers

NCT05627310

AIAD202204

Details and patient eligibility

About

Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).

Full description

Nasopharyngeal carcinoma (NPC) is an epithelial cancer derived from nasopharyngeal mucosa. Nasal endoscopy is the conventional examination for NPC screening. It is a major challenge for inexperienced endoscopists to accurately distinguish NPC and other benign dieseases. In this study, we collcet multi-center endoscopic images and train a deep learning model to detect NPC and indicate tumor location. Then, the model perfomance will be compared with endoscopists and be tested prospectively with external dataset.

Enrollment: 50,000 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

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.

Trial design

50,000 participants in 3 patient groups

Training Cohort

Description:

Nasopharyngeal endoscopic images collected from 8 hospitals all over China

Validation Cohort

Description:

Nasopharyngeal endoscopic images collected from 8 hospitals all over China

Treatment:

Other: Diagnostic

Testing Cohort

Description:

Nasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China

Treatment:

Other: Diagnostic