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

• Sponsor: Eye & ENT Hospital of Fudan University (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05627310
• Collaborator: Xiangya Hospital of Central South University (Other), The First Affiliated Hospital of Nanchang University (Other), Fujian Medical University Union Hospital (Other), Quan Zhou First Affiliated Hospital of Fujian Medical University (Other), First Affiliated Hospital of Guangxi Medical University (Other), The People' s Hospital of Guangxi Zhuang Autonomous Region (Other), The People' s Hospital of Jiangmen (Other)
• Enrollment: 50,000
• Locations: 8
• Anticipated Duration (Months): 17
• Patients Per Site: 6250
• Patients Per Site Per Month: 368.7

Study Details

Study Description

Brief Summary

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

Condition or Disease	Intervention/Treatment	Phase
Nasopharyngeal Carcinoma	Other: Diagnostic

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 Design

• Study Type: Observational
• Anticipated Enrollment: 50000 participants
• Observational Model: Other
• Time Perspective: Other
• Official Title: Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
• Actual Study Start Date: Nov 1, 2022
• Anticipated Primary Completion Date: Dec 31, 2023
• Anticipated Study Completion Date: Mar 31, 2024

Arms and Interventions

Arm	Intervention/Treatment
Training Cohort Nasopharyngeal endoscopic images collected from 8 hospitals all over China
Validation Cohort Nasopharyngeal endoscopic images collected from 8 hospitals all over China	Other: Diagnostic; Training dataset was used to train the deep learning model, which was validated and tested by external dataset.
Testing Cohort Nasopharyngeal endoscopic images prospectively collected from 8 hospitals all over China	Other: Diagnostic; Training dataset was used to train the deep learning model, which was validated and tested by external dataset.

Outcome Measures

Primary Outcome Measures

1. 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

1. 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.

2. 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.

Eligibility Criteria

Criteria

• Ages Eligible for Study: N/A and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Fujian Medical University Union Hospital	Fuzhou	Fujian	China
2	Quan Zhou First Affiliated Hospital of Fujian Medical University	Quanzhou	Fujian	China
3	The People' s Hospital of Jiangmen	Jiangmen	Guangdong	China
4	First Affiliated Hospital of Guangxi Medical University	Nanning	Guangxi	China
5	The People' s Hospital of Guangxi Zhuang Autonomous Region	Nanning	Guangxi	China
6	Xiangya Hospital of Central South University	Changsha	Hunan	China
7	The First Affiliated Hospital of Nanchang University	Nanchang	Jiangxi	China
8	Eye&ENT Hospital of Fudan University	Shanghai	Shanghai	China

Sponsors and Collaborators

• Eye & ENT Hospital of Fudan University
• Xiangya Hospital of Central South University
• The First Affiliated Hospital of Nanchang University
• Fujian Medical University Union Hospital
• Quan Zhou First Affiliated Hospital of Fujian Medical University
• First Affiliated Hospital of Guangxi Medical University
• The People' s Hospital of Guangxi Zhuang Autonomous Region
• The People' s Hospital of Jiangmen

Investigators

• Principal Investigator: Hongmeng Yu, MD PhD, Eye&ENT Hospital, Fudan University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Eye & ENT Hospital of Fudan University
• ClinicalTrials.gov Identifier: NCT05627310
• Other Study ID Numbers: AIAD202204
• First Posted: Nov 25, 2022
• Last Update Posted: Nov 25, 2022
• Last Verified: Nov 1, 2022

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Eye & ENT Hospital of Fudan University: nasopharyngeal carcinoma; deep learning; tumor screening
• Additional relevant MeSH terms: Carcinoma; Nasopharyngeal Carcinoma

Study Results

No Results Posted as of Nov 25, 2022