Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Gastric Cancer, Intestinal Metaplasia, Atrophic Gastritis. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Hong Kong
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data
Overview
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.
Primary outcome measures
- Gastric cancer and gastric dysplasia [Time frame: 20 years]
Secondary outcome measures (6)
- Overall accuracy of machine learning model [Time frame: 20 years]
- Sensitivity of machine learning model [Time frame: 20 years]
- Specificity of machine learning model [Time frame: 20 years]
- Positive predictive value of machine learning model [Time frame: 20 years]
- Negative predictive value of machine learning model [Time frame: 20 years]
- Area under the receiver operating characteristic curve of machine learning model [Time frame: 20 years]
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
\- none
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
Hong Kong · 1 center
- Prince of Wales Hospital — Shatin
Identifiers
NCT: NCT04840056 · 2021.082