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

Eye & ENT Hospital of Fudan University50,000 enrolled

Overview

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

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.

Study Type

OBSERVATIONAL

Enrollment

50,000

Conditions

Nasopharyngeal Carcinoma

Interventions

DiagnosticOTHER

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

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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.

Locations (8)

Fujian Medical University Union Hospital

Fuzhou, Fujian, China

RECRUITING

Quan Zhou First Affiliated Hospital of Fujian Medical University

Quanzhou, Fujian, China

RECRUITING

The People' s Hospital of Jiangmen

Outcomes

Primary Outcomes

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.

Time frame: baseline

Secondary Outcomes

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.

Time frame: 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.

Time frame: baseline

Central Contacts

Yu-Xuan Shi, MD PhD

CONTACT

+8618952373378 syxent@hotmail.com

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.