Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System
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
Status
Conditions
Conditions
Intervention / Treatment
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
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: yanqing Li, MD, PHD
- Phone Number: 0531182169385
- Email: liyanqing@sdu.edu.cn
Study Locations
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Shandong
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Jinan, Shandong, China, 250012
- Recruiting
- Department of Gastroenterology, Qilu Hospital, Shandong University
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Contact:
- Yanqing Li, PhD. MD.
- Phone Number: 18678827666
- Email: liyanqing@sdu.edu.cn
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
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
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Whole slide images of gastric biopsy specimens
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The diagnostic performance of AI model to assess the severity of intestinal metaplasia
Time Frame: 2 years
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The diagnostic performance of AI model to assess the severity of intestinal metaplasia in a single biopsy tissue slide: Accuracy, sensitivity, and specificity
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2 years
|
Secondary Outcome Measures
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
|
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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
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2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Chair: yanqing Li, MD, PHD, Qilu Hospital, Shandong University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2022-SDU-QILU-110
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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