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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Gastric Intestinal Metaplasia, Artificial Intelligence, Pathology, Gastric Cancer

Intervention / Treatment
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* Diagnostic Test: The diagnosis of Artificial Intelligence and pathologists


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 40 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Whole slide images of gastric biopsy specimens<br>Whole slide images of gastric biopsy specimens | Diagnostic Test: The diagnosis of Artificial Intelligence and pathologists<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The diagnostic performance of AI model to assess the severity of intestinal metaplasia | 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 | 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 | Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas | 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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gastric cancer, Gastric Intestinal Metaplasia, Digital Pathology, Whole Slide Image