Spots Global Cancer Trial Database for Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images
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Trial Identification
Brief Title: Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images
Official Title: Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
Study ID: NCT05627310
Conditions
Interventions
Study Description
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.
Eligibility
Minimum Age:
Eligible Ages: CHILD, ADULT, OLDER_ADULT
Sex: ALL
Healthy Volunteers: No
Locations
Fujian Medical University Union Hospital, Fuzhou, Fujian, China
Quan Zhou First Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
The People' s Hospital of Jiangmen, Jiangmen, Guangdong, China
First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
The People' s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
Xiangya Hospital of Central South University, Changsha, Hunan, China
The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
Eye&ENT Hospital of Fudan University, Shanghai, Shanghai, China
Contact Details
Name: Hongmeng Yu, MD PhD
Affiliation: Eye&ENT Hospital, Fudan University
Role: PRINCIPAL_INVESTIGATOR
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