Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis

NCT04840056 | Recruiting DMC

• 1 other identifier: 2021.082
• Study Type: observational
• Target: 1,300
• Locations: 1 country
• Sites: 1

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

Conditions

Gastric Cancer; Intestinal Metaplasia; Atrophic Gastritis

Keywords

artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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).

  20 years

Secondary Outcomes (6)

• 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

• Negative predictive value of machine learning model

  20 years

Study Arms (2)

Intestinal Metaplasia

patient with history of histologically proven gastric intestinal metaplasia

Atrophic gastritis

patient with history of histologically proven atrophic gastritis

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Adults \>= 18 years of age
• Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)

You may not qualify if:

• \- none

Sponsors & Collaborators

• Chinese University of Hong Kong: lead

Study Sites (1)

Prince of Wales Hospital

Shatin, New Territories, Hong Kong

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms; Gastritis, Atrophic

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Gastritis; Gastroenteritis

Central Study Contacts

Felix Sia

CONTACT

+85226370428; felix.sia@cuhk.edu.hk

Thomas Lam

CONTACT

+85226370428; thomas.lam@cuhk.edu.hk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 23, 2021
• First Posted: April 9, 2021
• Study Start: April 15, 2021
• Primary Completion: December 1, 2025
• Study Completion: December 31, 2025
• Last Updated: August 29, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)