Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 50,000
- 试验地点
- 8
- 主要终点
- Area under the receiver operating characteristic curve of the deep learning algorithm
研究概览
简要总结
Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •images with spots from lens flares or stains, and overexposure were excluded from further analysis.
- •image can not expose most part of lesion clearly.
结局指标
主要结局
Area under the receiver operating characteristic curve of the deep learning algorithm
时间窗: 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.
次要结局
- Sensitivity of the deep learning system(baseline)
- Specificity of the deep learning system(baseline)