Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis (GIMA)

Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data

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

Study Overview

• Status: Recruiting
• Conditions: Gastric Cancer; Intestinal Metaplasia; Atrophic Gastritis

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.

• Study Type: Observational
• Enrollment (Anticipated): 1300

Contacts and Locations

• Study Contact: Name: Felix Sia; Phone Number: +85226370428; Email: felix.sia@cuhk.edu.hk
• Study Contact Backup: Name: Thomas Lam; Phone Number: +85226370428; Email: thomas.lam@cuhk.edu.hk

Study Locations

• Hong Kong
  • New Territories
    • Shatin, New Territories, Hong Kong
      • Recruiting
      • Prince of Wales Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Intestinal Metaplasia; patient with history of histologically proven gastric intestinal metaplasia
Atrophic gastritis; patient with history of histologically proven atrophic gastritis

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall accuracy of machine learning model	Overall accuracy of machine learning models will be evaluated	20 years
Sensitivity of machine learning model	Sensitivity of machine learning model will be evaluated	20 years
Specificity of machine learning model	Specificity of machine learning model will be evaluated	20 years
Positive predictive value of machine learning model	Positive predictive value of machine learning model will be evaluated	20 years
Negative predictive value of machine learning model	Negative predictive value of machine learning model will be evaluated	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	20 years

Collaborators and Investigators

• Sponsor: Chinese University of Hong Kong

Study record dates

Study Major Dates

• Study Start (ACTUAL): April 15, 2021
• Primary Completion (ANTICIPATED): December 1, 2025
• Study Completion (ANTICIPATED): December 31, 2025

Study Registration Dates

• First Submitted: March 23, 2021
• First Submitted That Met QC Criteria: April 7, 2021
• First Posted (ACTUAL): April 9, 2021

Study Record Updates

• Last Update Posted (ACTUAL): February 8, 2023
• Last Update Submitted That Met QC Criteria: February 6, 2023
• Last Verified: February 1, 2023

More Information

Terms related to this study

• Keywords: artificial intelligence
• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Gastroenteritis; Pathological Conditions, Anatomical; Stomach Neoplasms; Gastritis; Atrophy; Metaplasia; Gastritis, Atrophic
• Other Study ID Numbers: 2021.082

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: There is no plan to share IPD with other researchers

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No