Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System
- Conditions
- Gastric Intestinal MetaplasiaGastric CancerArtificial IntelligencePathology
- Interventions
- Diagnostic Test: The diagnosis of Artificial Intelligence and pathologists
- Registration Number
- NCT05447221
- Lead Sponsor
- Shandong University
- Brief Summary
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
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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 150
- patients aged 40-75 years who undergo the gastroscopy examination and biopsy
- 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 & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Whole slide images of gastric biopsy specimens The diagnosis of Artificial Intelligence and pathologists Whole slide images of gastric biopsy specimens
- Primary Outcome Measures
Name Time Method The diagnostic performance of AI model to assess the severity of intestinal metaplasia 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
- Secondary Outcome Measures
Name Time Method Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas 2 years Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas
Accuracy of the digital pathological AI model to identify tumor regions 2 years Accuracy of the digital pathological AI model in identifying tumor regions in the whole slide images
Trial Locations
- Locations (1)
Department of Gastroenterology, Qilu Hospital, Shandong University
🇨🇳Jinan, Shandong, China