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

Gastric Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05447221
• Other study ID #: 2022-SDU-QILU-110
• Status: Recruiting
• Start date: August 1, 2022
• Est. completion date: December 31, 2023

Study information

• Verified date: September 2023
• Source: Shandong University
• Contact: Yanqing Li, MD, PhD
• Phone: 0531182169385
• Email: liyanqing[@]sdu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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 information / eligibility

• Status: Recruiting
• Enrollment: 150
• Est. completion date: December 31, 2023
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 40 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Artificial Intelligence
• Gastric Cancer
• Gastric Intestinal Metaplasia
• Metaplasia
• Pathology
• Stomach Neoplasms

Intervention

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.

Locations

Country	Name	City	State
China	Department of Gastroenterology, Qilu Hospital, Shandong University	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	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