Advancements and Innovations in Cancer Research: Pioneering Pathways to Healing

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

Secondary Outcome Measures :

1. Overall accuracy of machine learning model [ Time Frame: 20 years ]Overall accuracy of machine learning models will be evaluated

2. Sensitivity of machine learning model [ Time Frame: 20 years ]Sensitivity of machine learning model will be evaluated

3. Specificity of machine learning model [ Time Frame: 20 years ]Specificity of machine learning model will be evaluated

4. Positive predictive value of machine learning model [ Time Frame: 20 years ]Positive predictive value of machine learning model will be evaluated

5. Negative predictive value of machine learning model [ Time Frame: 20 years ]Negative predictive value of machine learning model will be evaluated

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.