Detection and Biopsy Guidance of Nasopharyngeal Carcinoma Based on Artificial Intelligence and 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
- 100
- 试验地点
- 5
- 主要终点
- Aera under the receiver operating characteristic curve (AUC)
研究概览
简要总结
Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for nasopharyngeal carcinoma identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal carcinoma upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies and guide biopsy under endoscopic examination based on deep learning.
详细描述
Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for nasopharyngeal carcinoma identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal carcinoma upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies and guide biopsy under endoscopic examination based on deep learning.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •The patient was found to have a nasopharyngeal lesion through the nasopharyngeal endoscopy and the clinicans considered it necessary to perform an biopsy.
- •Hemilateral lesion with limited size.
排除标准
- •Patients with nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, etc. who have already been treated.
结局指标
主要结局
Aera under the receiver operating characteristic curve (AUC)
时间窗: three months
AUC of an deep learning-based model in discriminating nasopharyngeal carcinoma from bengin lesion.
次要结局
- Accuray(three months)
研究者
Di Dong
Professor
Chinese Academy of Sciences