Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,300
- 试验地点
- 1
- 主要终点
- Gastric cancer and gastric dysplasia
研究概览
简要总结
The primary objectives of this study are:
- To identify clinical or histological factors associated with gastric cancer development in patients with IM and AG
- To establish a machine learning algorithm for prediction of future gastric cancer risks and individual risk stratification in patient with IM and AG
详细描述
This is a two-part retrospective study including a clinical data part and a pathology part. A training cohort will be developed from approximately 70% of included cases. It will be followed by a validation cohort with the remaining cases.
Clinical data will be collected retrospectively using the Clinical Data Analysis and Reporting System (CDARS) and Clinical management System (CMS). A cluster-wide cohort (New Territories East Cluster, NTEC) consisting of patients with history of histologically-proven gastric IM and AG will be identified and included for subsequent analysis. The data collection period for the retrospective data will be 2000-2020.
Histology slides will be retrieved retrospectively when available (within NTEC). Whole slide imaging technique will be utilized for the development of training and validation cohorts with machine learning algorithms in the pathology part.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 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
- •Adults >= 18 years of age
- •Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)
排除标准
- 未提供
结局指标
主要结局
Gastric cancer and gastric dysplasia
时间窗: 20 years
The primary endpoint is the incidence of gastric cancer (intestinal-type) and gastric dysplasia (low grade and high grade dysplasia).
次要结局
- Negative predictive value of machine learning model(20 years)
- Overall accuracy of machine learning model(20 years)
- Sensitivity of machine learning model(20 years)
- Specificity of machine learning model(20 years)
- Positive predictive value of machine learning model(20 years)
- Area under the receiver operating characteristic curve of machine learning model(20 years)
研究者
Louis Ho Shing Lau
Principal Investigator
Chinese University of Hong Kong