Accurate Diagnosis of the Invasion Depth in Early Esophageal Squamous Cell Carcinoma by a Deep Neural Network Analysis of Narrow-band Imaging Endoscopy Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 500
- 主要终点
- Accurate diagnose the invasion depth of early esophageal squamous cell carcinoma by endoscopy NBI images through deep neural network analysis
研究概览
简要总结
The goal of this observational study is to accurate diagnose the stage of esophageal squamous cell carcinoma in order to help physicians to decide the appropriate clinical treatment. The main question it aims to answer is:
• To get early accurate diagnosis of the invasion depth of esophageal squamous cell carcinoma by narrow-band imaging endoscopy data.
Participants' clinical informations from routine examinations and treatments will be collected, there will be no harm to participants.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
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
- •Age ≥ 18 years old, regardless of gender;
- •Performing esophageal ESD surgery due to esophageal mucosal lesions;
- •Pathological evaluation of ESD specimens.
排除标准
- •Pathological examination after ESD surgery ruled out esophageal squamous cell carcinoma.
结局指标
主要结局
Accurate diagnose the invasion depth of early esophageal squamous cell carcinoma by endoscopy NBI images through deep neural network analysis
时间窗: 2024/12/31
We will compare the predictive performance of InvaDepNet before and after incorporating data generated using GAN and demonstrated that including the generated data in the training dataset effectively improves the accuracy of the predictive model. Additionally, we will train six commonly used CNN models on two datasets with different shooting angles (including NBI without magnifying and NBI with magnifying), and we will propose a ResNet model to analysis the clinical informations combine with NBI images.
次要结局
未报告次要终点