Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy

NCT ID: NCT04563416

Last Updated: 2020-09-24

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 80 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-07-10
• Study Completion Date: 2020-12-30

Brief Summary

Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.

Detailed Description

Gastric cancer is the third most common cause of cancer-associated deaths worldwide especially in Asia.Early detection and treatment would cure the disease with 5-year survival rate greater than 90%.However, the sensitivity of conventional endoscopy with white-light imaging (C-WLI) in diagnosis of early gastric cancers (EGCs) is merely 40%. Magnifying image-enhanced endoscopy (IEE) techniques such as magnifying narrow band imaging (M-NBI) have been developed and 2 RCT report that white-light imaging combine with M-NBI can increase the sensitivity to 95%. The strategy that using white-light imaging to detect the suspicious lesion and using M-IEE techniques to make a diagnosis of early gastric cancer is recommend in screening endoscopy.

Optical enhancement (OE) which is one of the M-IEE techniques was developed by HOYA Co. (Tokyo, Japan) . This technology combines digital signal processing and optical filterers to clear display of mucosal microsurface (MS) and microvessel (MV). The advantage of OE is to overcome the darkness of NBI which leads to less usefulness for detect-ability in the full extended gastrointestinal lumen.Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner, and expertise with sub-optimal inter-observer agreement is essential for the use of M-IEE.

Nowadays, Artificial intelligence (AI) using deep machine learning has made a major breakthrough in gastroenterology, which using gradient descent method and backpropagation to automatically extract specific images features. The diagnostic accuracy in identifying upper gastrointestinal cancer was 0.955 in C-WLI . Polyps can be identified in real time with 96% accuracy in screening colonoscopy. AI show an outstanding application in detection and diagnosis.

This study aims to develop a M-OE assistance model in the diagnosis of EGCs by distinguishing cancer or not.

Conditions

Artificial Intelligence; Optical Enhancement Endoscopy; Magnifying Endoscopy

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

Patients who need undergo magnifying endoscopy

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• patients receive optical magnifying OE endoscopy examination

Exclusion Criteria

• Patients with advanced cancer, lymphoma,active stage of ulcer and artificial ulcer after ESD were excluded.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shandong University

OTHER

Sponsor Role: lead

Responsible Party

Yanqing Li

Vice president of Qilu Hospital

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Yanqing Li, PHD

Role: PRINCIPAL_INVESTIGATOR

Study Principal Investigator Qilu Hospital, Shandong University

Locations

Department of Gastroenterology, Qilu Hospital, Shandong University

Jinan, Shandong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Yanqing Li, PHD

Role: CONTACT

liyanqing@sdu.edu.cn

86-531-82169236

Facility Contacts

Yanqing Li, PhD. MD.

Role: primary

liyanqing@sdu.edu.cn

18678827666

References

Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol. 2022 Aug 5;12:945904. doi: 10.3389/fonc.2022.945904. eCollection 2022.

Reference Type: DERIVED

PMID: 35992850 (View on PubMed)

Other Identifiers

2018SDU-QILU-3

Identifier Type: -

Identifier Source: org_study_id