Clinical trial NCT05627310: 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 type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Nasopharyngeal Carcinoma
Keywords: nasopharyngeal carcinoma
deep learning
tumor screening
Lead sponsor: Eye & ENT Hospital of Fudan University
Sponsor class: OTHER
Collaborators: name: Xiangya Hospital of Central South University
name: The First Affiliated Hospital of Nanchang University
name: Fujian Medical University Union Hospital
name: Quan Zhou First Affiliated Hospital of Fujian Medical University
name: First Affiliated Hospital of Guangxi Medical University
name: People's Hospital of Guangxi Zhuang Autonomous Region
name: The People' s Hospital of Jiangmen
First posted: 2022-11-25
Enrollment: 50000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: PROBABILITY_SAMPLE
Study population: Nasopharyngeal endoscopic images were collected from 8 hospital in NPC epidemic area all over China.

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 criteria: 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.

Primary outcomes:
measure: Area under the receiver operating characteristic curve of the deep learning algorithm
timeFrame: baseline
Secondary outcomes:
measure: Sensitivity of the deep learning system
timeFrame: baseline
measure: Specificity of the deep learning system
timeFrame: baseline
Other outcomes:
Locations:
city: Fuzhou
country: China
facility: Fujian Medical University Union Hospital
state: Fujian
city: Quanzhou
country: China
facility: Quan Zhou First Affiliated Hospital of Fujian Medical University
state: Fujian
city: Jiangmen
country: China
facility: The People' s Hospital of Jiangmen
state: Guangdong
city: Nanning
country: China
facility: First Affiliated Hospital of Guangxi Medical University
state: Guangxi
city: Nanning
country: China
facility: The People' s Hospital of Guangxi Zhuang Autonomous Region
state: Guangxi
city: Changsha
country: China
facility: Xiangya Hospital of Central South University
state: Hunan
city: Nanchang
country: China
facility: The First Affiliated Hospital of Nanchang University
state: Jiangxi
city: Shanghai
country: China
facility: Eye&ENT Hospital of Fudan University
state: Shanghai
Study officials:
affiliation: Eye&ENT Hospital, Fudan University
name: Hongmeng Yu, MD PhD
role: PRINCIPAL_INVESTIGATOR
References: