Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System

September 2, 2023 updated by: Yanqing Li, Shandong University

Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System: a Diagnostic Test

The OLGIM staging system is highly recommended for a comprehensive assessment of GIM severity to evaluate patients' gastric cancer risk. However, its need to take at least 4 biopsies is not clinically feasible due to a serious shortage of pathologists compared with the large number of gastric cancer screening population.

We plan to develop a Digital Pathology artificial intelligence diagnosis system (DPAIDS), to automatically identify tumor areas in whole slide images(WSI) and quickly and accurately quantify the severity of intestinal metaplasia according to the proportion of intestinal metaplasia areas.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Gastric cancer is the fifth most prevalent malignancy and the third most deadly worldwide, and intestinal metaplasia (IM) is a common precancerous state that is closely associated with gastric carcinogenesis .The OLGIM staging system is highly recommended for a comprehensive assessment of GIM severity to evaluate patients' gastric cancer risk. However, its need to take at least four biopsies is not clinically feasible due to a serious shortage of pathologists compared with the large number of gastric cancer screening population. Developing automated screening methods can reduce the heavy diagnostic workload. With advances in digital pathology scanning devices and deep learning technologies, whole-slide images (WSI) have been used to develop automated cancer diagnostic systems.

We plan to develop a Digital Pathology artificial intelligence diagnosis system (DPAIDS), to automatically identify tumor areas in whole slide images(WSI) and quickly and accurately quantify the severity of intestinal metaplasia according to the proportion of intestinal metaplasia areas. Then biopsies will be prospectively collected and prepared as WSI for model validation.

Study Type

Observational

Enrollment (Estimated)

150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250012
        • Recruiting
        • Department of Gastroenterology, Qilu Hospital, Shandong University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

40 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study

Description

Inclusion Criteria:

  • patients aged 40-75 years who undergo the gastroscopy examination and biopsy

Exclusion Criteria:

  • patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
  • patients with previous surgical procedures on the stomach
  • patients with contraindications to biopsy
  • patients who refuse to sign the informed consent form

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Whole slide images of gastric biopsy specimens
Diagnostic test: The diagnosis of Artificial Intelligence and pathologists
Pathologists and AI will assess the severity of intestinal metaplasia and judge the tumor area of whole slide images of gastric biopsy specimens independently. In addition, the pathologists can not see the diagnosis of AI.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The diagnostic performance of AI model to assess the severity of intestinal metaplasia
Time Frame: 2 years
The diagnostic performance of AI model to assess the severity of intestinal metaplasia in a single biopsy tissue slide: Accuracy, sensitivity, and specificity
2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of the digital pathological AI model to identify tumor regions
Time Frame: 2 years
Accuracy of the digital pathological AI model in identifying tumor regions in the whole slide images
2 years
Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas
Time Frame: 2 years
Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas
2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shandong University

Investigators

  • Study Chair: yanqing Li, MD, PHD, Qilu Hospital, Shandong University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2022

Primary Completion (Estimated)

December 31, 2023

Study Completion (Estimated)

December 31, 2023

Study Registration Dates

First Submitted

July 3, 2022

First Submitted That Met QC Criteria

July 3, 2022

First Posted (Actual)

July 7, 2022

Study Record Updates

Last Update Posted (Actual)

September 6, 2023

Last Update Submitted That Met QC Criteria

September 2, 2023

Last Verified

September 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022-SDU-QILU-110

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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