Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Gastric Cancer
- Sponsor
- Chinese University of Hong Kong
- Enrollment
- 1300
- Locations
- 1
- Primary Endpoint
- Gastric cancer and gastric dysplasia
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
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
Detailed Description
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.
Investigators
Louis Ho Shing Lau
Principal Investigator
Chinese University of Hong Kong
Eligibility Criteria
Inclusion Criteria
- •Adults \>= 18 years of age
- •Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Gastric cancer and gastric dysplasia
Time Frame: 20 years
The primary endpoint is the incidence of gastric cancer (intestinal-type) and gastric dysplasia (low grade and high grade dysplasia).
Secondary Outcomes
- 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)