Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

NCT04040374 | Completed

• 1 other identifier: 11931-(1)
• Study Type: interventional
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Title: A single-center, retrospective randomized controlled trial of artificial intelligence (AI) versus expert endoscopists for diagnosis of gastric cancer in patients who underwent upper gastrointestinal endoscopy. Précis: this single-center, retrospective randomized controlled trial will include 500 outpatients who underwent upper gastrointestinal endoscopy for gastric cancer screening and will compare the diagnostic detection rate for gastric cancer of AI and expert endoscopists. Objectives Primary Objective: to evaluate the diagnostic detection rate for gastric cancer of AI and expert endoscopists. Secondary Objectives: to determine whether AI is not inferior to expert endoscopists in terms of the number of images analyzed for diagnosis of gastric cancer and intersection over union (IOU), and the detection rate of diagnosis of early and advanced gastric cancer. Endpoints Primary Endpoint: diagnosis of gastric cancer. Secondary Endpoints: image based diagnosis of gastric cancer and IOU. Population: in total, 500 males and females aged ≥ 20 years who underwent upper gastrointestinal endoscopy for screening of gastric cancer at a single hospital in Japan. Describe the Intervention: AI-based diagnosis of gastric cancer based on upper gastrointestinal endoscopy images. Study Duration: 3 months.

Conditions

Gastric Cancer

Keywords

artificial intelligence; gastric cancer

Outcome Measures

Primary Outcomes (1)

• Per patient diagnosis of gastric cancer

  Number of Participants

  Up to 6 weeks from study start

Secondary Outcomes (5)

• Number of images analyzed for diagnosis of gastric cancer

  Up to 6 weeks from study start

• Intersection over union (IOU) of gastric lesions

  Up to 6 weeks from study start

• Diagnosis of advanced gastric cancer

  Up to 6 weeks from study start

• Diagnosis of early gastric cancer

  Up to 6 weeks from study start

• Agreement on image and IOU based diagnosis of gastric cancer between AI and expert endoscopists

  Up to 12 weeks from study start

Study Arms (2)

AI-based diagnosis

EXPERIMENTAL

• AI-based diagnosis will be performed based on analysis of endoscopic images (Olympus Optical, Tokyo, Japan). The investigators will use the Single Shot MultiBox Detector (SSD), a deep neural network architecture (https://arxiv.org/abs/1512.02325), and an optimal diagnostic cutoff from a prior report2. The AI system reviewed endoscopy images and reported those in which gastric cancer was detected, together with the coordinates (X, Y) of the lesions.

Diagnostic Test: AI-based diagnosis

Expert endoscopist diagnosis

ACTIVE COMPARATOR

The expert endoscopists are two physicians with experience of more than 20,000 endoscopies. The expert endoscopists will review the endoscopy images of each patient for 5 min. They will then report endoscopy images in which gastric cancer was detected and manually annotate the lesions in those images.

Diagnostic Test: The expert endoscopists-based diagnosis

Interventions

AI-based diagnosis DIAGNOSTIC_TEST

AI-based diagnosis will be performed based on analysis of endoscopic images (Olympus Optical, Tokyo, Japan). The investigators will use the Single Shot MultiBox Detector (SSD), a deep neural network architecture (https://arxiv.org/abs/1512.02325), and an optimal diagnostic cutoff from a prior report2. The AI system reviewed endoscopy images and reported those in which gastric cancer was detected, together with the coordinates (X, Y) of the lesions.

AI-based diagnosis

The expert endoscopists-based diagnosis DIAGNOSTIC_TEST

The expert endoscopists are two physicians with experience of more than 20,000 endoscopies. The expert endoscopists will review the endoscopy images of each patient for 5 min. They will then report endoscopy images in which gastric cancer was detected and manually annotate the lesions in those images.

Expert endoscopist diagnosis

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Males or females aged ≥ 20 years who underwent upper gastrointestinal endoscopy at Tokyo University Hospital during 2018.
• Informed optout consent, obtained from each patient before completion of the study.

You may not qualify if:

• Patients who underwent gastrectomy.
• Patients who underwent transnasal upper gastrointestinal endoscopy.

Sponsors & Collaborators

• Tokyo University: lead

Study Sites (1)

Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo

Tokyo, 1138655, Japan

Location

Related Publications (3)

• Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014 Sep 11;513(7517):202-9. doi: 10.1038/nature13480. Epub 2014 Jul 23.

  PMID: 25079317 BACKGROUND

• Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018 Jul;21(4):653-660. doi: 10.1007/s10120-018-0793-2. Epub 2018 Jan 15.

  PMID: 29335825 BACKGROUND

• Niikura R, Aoki T, Shichijo S, Yamada A, Kawahara T, Kato Y, Hirata Y, Hayakawa Y, Suzuki N, Ochi M, Hirasawa T, Tada T, Kawai T, Koike K. Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy. 2022 Aug;54(8):780-784. doi: 10.1055/a-1660-6500. Epub 2022 May 4.

  PMID: 34607377 DERIVED

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Study Officials

• Ryota Niikura, MD

  Tokyo University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Associate

Study Record Dates

• First Submitted: July 19, 2019
• First Posted: July 31, 2019
• Study Start: July 1, 2019
• Primary Completion: October 1, 2019
• Study Completion: November 16, 2019
• Last Updated: November 20, 2019

Record last verified: 2019-11

Data Sharing

• IPD Sharing: Will not share

Locations

JP (1)