Atrophic Gastritis, Gastric Cancer, Intestinal Metaplasia
Conditions
Keywords
artificial intelligence
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.
Interventions
None listed
Sponsors
Chinese University of Hong Kong
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
\- none
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Gastric cancer and gastric dysplasia | 20 years | The primary endpoint is the incidence of gastric cancer (intestinal-type) and gastric dysplasia (low grade and high grade dysplasia). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of machine learning model | 20 years | Sensitivity of machine learning model will be evaluated |
| Specificity of machine learning model | 20 years | Specificity of machine learning model will be evaluated |
| Overall accuracy of machine learning model | 20 years | Overall accuracy of machine learning models will be evaluated |
| Negative predictive value of machine learning model | 20 years | Negative predictive value of machine learning model will be evaluated |
| Area under the receiver operating characteristic curve of machine learning model | 20 years | Area under the receiver operating characteristic curve of machine learning model will be evaluated |
| Positive predictive value of machine learning model | 20 years | Positive predictive value of machine learning model will be evaluated |
Countries
Hong Kong
Contacts
Primary ContactFelix Sia
Backup ContactThomas Lam
Outcome results
None listed