Nasopharyngeal Carcinoma
Conditions
Keywords
nasopharyngeal carcinoma, deep learning, tumor screening
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.
Interventions
OTHERDiagnostic
Training dataset was used to train the deep learning model, which was validated and tested by external dataset.
Sponsors
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
People's Hospital of Guangxi Zhuang Autonomous Region
The People' s Hospital of Jiangmen
Eye & ENT Hospital of Fudan University
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
Primary ContactYu-Xuan Shi, MD PhD
Outcome results
None listed