Clinical trial NCT05447221: Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System
Official title: Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System: a Diagnostic Test
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Gastric Intestinal Metaplasia
Artificial Intelligence
Pathology
Gastric Cancer
Keywords: gastric cancer
Gastric Intestinal Metaplasia
Digital Pathology
Whole Slide Image
Lead sponsor: Shandong University
Sponsor class: OTHER
First posted: 2022-07-07
Enrollment: 150
Sex: ALL
Minimum age (years): 40
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
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

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.

Eligibility criteria: 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

Primary outcomes:
measure: The diagnostic performance of AI model to assess the severity of intestinal metaplasia
timeFrame: 2 years
Secondary outcomes:
measure: Accuracy of the digital pathological AI model to identify tumor regions
timeFrame: 2 years
measure: Accuracy of digital pathological AI models to identify glands, mucosal epithelium, and intestinal metaplasia in non-neoplastic areas
timeFrame: 2 years
Other outcomes:
Locations:
city: Jinan
country: China
facility: Department of Gastroenterology, Qilu Hospital, Shandong University
state: Shandong
Study officials:
affiliation: Qilu Hospital, Shandong University
name: Yanqing Li, MD, PhD
role: STUDY_CHAIR
References: