An observational study in Gastric Cancer, Intestinal Metaplasia and Atrophic Gastritis, sponsored by Chinese University of Hong Kong. Recruiting at 1 site in Hong Kong. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-22.
Sponsored by Chinese University of Hong Kong · Observational
The primary objectives of this study are:
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.
3,399 studies on the registry are indexed under Stomach Neoplasms; 992 are open to participants now.
This study's planned enrollment of 1,300 is above the median of 278 across 790 observational studies indexed under Stomach Neoplasms.
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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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
- none
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
The diagnosis was confirmed by looking at a sample under a microscope: tissue from a biopsy or surgery (histology), or cells from a fluid or needle sample (cytology).
Your pathology report is the document that shows this.
Histologically or cytologically confirmed
The diagnosis was confirmed by looking at a sample under a microscope: tissue from a biopsy or surgery (histology), or cells from a fluid or needle sample (cytology).
Your pathology report is the document that shows this.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
patient with history of histologically proven gastric intestinal metaplasia
patient with history of histologically proven atrophic gastritis
Gastric cancer and gastric dysplasia
The primary endpoint is the incidence of gastric cancer (intestinal-type) and gastric dysplasia (low grade and high grade dysplasia).
Time frame: 20 years
Overall accuracy of machine learning model
Overall accuracy of machine learning models will be evaluated
Time frame: 20 years
Sensitivity of machine learning model
Sensitivity of machine learning model will be evaluated
Time frame: 20 years
Specificity of machine learning model
Specificity of machine learning model will be evaluated
Time frame: 20 years
Positive predictive value of machine learning model
Positive predictive value of machine learning model will be evaluated
Time frame: 20 years
Negative predictive value of machine learning model
Negative predictive value of machine learning model will be evaluated
Time frame: 20 years
Area under the receiver operating characteristic curve of machine learning model
Area under the receiver operating characteristic curve of machine learning model will be evaluated
Time frame: 20 years
Plan to share: No — There is no plan to share IPD with other researchers
No publications or documents are linked to this record.
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Chinese University of Hong Kong →