NCT05925764招募中不适用
Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma
Shanghai Pulmonary Hospital, Shanghai, China3 个研究点 分布在 1 个国家目标入组 200 人开始时间: 2024年10月15日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 200
- 试验地点
- 3
- 主要终点
- Agreement rate of the IASLC grading system
研究概览
简要总结
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.
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 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ranging from 18-85 years old;
- •Pathological confirmation of primary lung adenocarcinoma after surgery;
- •Obtained written informed consent.
排除标准
- •Multiple lung lesions;
- •Poor quality of whole slide images;
- •Mucinous adenocarcinomas and variants;
- •Participants who have received neoadjuvant therapy.
结局指标
主要结局
Agreement rate of the IASLC grading system
时间窗: 2024.11.01-2024.12.31
Agreement rate between the deep learning model and pathologists in diagnosing the IASLC grade of lung adenocarcinoma.
次要结局
- Agreement rate of the predominant subtypes(2024.11.01-2024.12.31)
研究者
Chang Chen
Professor
Shanghai Pulmonary Hospital, Shanghai, China
研究点 (3)
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