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

Nasopharyngeal Carcinoma Clinical Trial

Official title:

Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05627310
• Other study ID #: AIAD202204
• Status: Recruiting
• Start date: November 1, 2022
• Est. completion date: March 31, 2024

Study information

• Verified date: November 2022
• Source: Eye & ENT Hospital of Fudan University
• Contact: Yu-Xuan Shi, MD PhD
• Phone: +8618952373378
• Email: syxent[@]hotmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50000
• Est. completion date: March 31, 2024
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Carcinoma
• Nasopharyngeal Carcinoma

Intervention

Other:
• Diagnostic
Training dataset was used to train the deep learning model, which was validated and tested by external dataset.

Locations

Country	Name	City	State
China	Xiangya Hospital of Central South University	Changsha	Hunan
China	Fujian Medical University Union Hospital	Fuzhou	Fujian
China	The People' s Hospital of Jiangmen	Jiangmen	Guangdong
China	The First Affiliated Hospital of Nanchang University	Nanchang	Jiangxi
China	First Affiliated Hospital of Guangxi Medical University	Nanning	Guangxi
China	The People' s Hospital of Guangxi Zhuang Autonomous Region	Nanning	Guangxi
China	Quan Zhou First Affiliated Hospital of Fujian Medical University	Quanzhou	Fujian
China	Eye&ENT Hospital of Fudan University	Shanghai	Shanghai

Sponsors (8)

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

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area under the receiver operating characteristic curve of the deep learning algorithm	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.	baseline
Secondary	Sensitivity of the deep learning system	The investigators will calculate the sensitivity of deep learning algorithm and compare this index between deep learning system and human doctors.	baseline
Secondary	Specificity of the deep learning system	The investigators will calculate the specificity of deep learning algorithm and compare this index between deep learning system and human doctors.	baseline