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

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.

Study Overview

• Status: Unknown
• Conditions: Artificial Intelligence; Optical Enhancement Endoscopy; Magnifying Endoscopy

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.

• Study Type: Observational
• Enrollment (Anticipated): 80

Contacts and Locations

Study Locations

• China
  • Shandong
    • Jinan, Shandong, China, 250012
      • Recruiting
      • Department of Gastroenterology, Qilu Hospital, Shandong University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Consecutive patients suspected of early gastric cancer and receive optical magnifying OE endoscopy examination.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Patients who need undergo magnifying endoscopy

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
the diagnosis efficiency of the computer-assist diagnosis tool	the sensitivity, specificity and accuracy of the computer-assist diagnosis tool	12 months

Collaborators and Investigators

• Sponsor: Shandong University
• Investigators: Principal Investigator: Yanqing Li, PHD, Study Principal Investigator Qilu Hospital, Shandong University

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): July 10, 2020
• Primary Completion (Anticipated): November 30, 2020
• Study Completion (Anticipated): December 30, 2020

Study Registration Dates

• First Submitted: September 22, 2020
• First Submitted That Met QC Criteria: September 22, 2020
• First Posted (Actual): September 24, 2020

Study Record Updates

• Last Update Posted (Actual): September 24, 2020
• Last Update Submitted That Met QC Criteria: September 22, 2020
• Last Verified: September 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Stomach Neoplasms
• Other Study ID Numbers: 2018SDU-QILU-3

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No