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

Recruiting

• Conditions: Nasopharyngeal Carcinoma
• Interventions: Other: Diagnostic

• Registration Number: NCT05627310
• Lead Sponsor: Eye & ENT Hospital of Fudan University

Brief Summary

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

Detailed 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.

Study Timeline

• Study First Submitted: 2022-07-11
• Status Verified: 2022-11
• Study Start: 2022-11-01
• Study First Submitted QC: 2022-11-23
• Last Update Submitted: 2022-11-23
• Study First Posted: 2022-11-25
• Last Update Posted: 2022-11-25
• Primary Completion: 2023-12-31
• Study Completion: 2024-03-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 50000

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.

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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.

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Testing Cohort	Diagnostic	Nasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China
Validation Cohort	Diagnostic	Nasopharyngeal endoscopic images collected from 8 hospitals all over China

Primary Outcome Measures

Name	Time	Method
Area under the receiver operating characteristic curve of the deep learning algorithm	baseline	The investigators will calculate the area under the receiver operating characteristic curve of deep learning algorithm and compare this index between deep learning system and human doctors.

Secondary Outcome Measures

Name	Time	Method
Sensitivity of the deep learning system	baseline	The investigators will calculate the sensitivity of deep learning algorithm and compare this index between deep learning system and human doctors.
Specificity of the deep learning system	baseline	The investigators will calculate the specificity of deep learning algorithm and compare this index between deep learning system and human doctors.

Trial Locations

Locations (8)

First Affiliated Hospital of Guangxi Medical University; 🇨🇳 Nanning, Guangxi, China

The People' s Hospital of Guangxi Zhuang Autonomous Region; 🇨🇳 Nanning, Guangxi, China

Eye&ENT Hospital of Fudan University; 🇨🇳 Shanghai, Shanghai, China

Xiangya Hospital of Central South University; 🇨🇳 Changsha, Hunan, China

The People' s Hospital of Jiangmen; 🇨🇳 Jiangmen, Guangdong, China

The First Affiliated Hospital of Nanchang University; 🇨🇳 Nanchang, Jiangxi, China

Quan Zhou First Affiliated Hospital of Fujian Medical University; 🇨🇳 Quanzhou, Fujian, China

Fujian Medical University Union Hospital; 🇨🇳 Fuzhou, Fujian, China