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

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

Detailed Description
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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.

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

Conditions
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Nasopharyngeal Carcinoma

Intervention / Treatment
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* Other: Diagnostic


Participation Criteria
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Eligibility Criteria
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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.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

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

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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nasopharyngeal carcinoma, deep learning, tumor screening