Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

Gastric Cancer Clinical Trial

Official title:

A Single-center, Retrospective, Open Label, Randomized Controlled Trial of Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer in Patients Who Underwent Upper Gastrointestinal Endoscopy

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04040374
• Other study ID #: 11931-(1)
• Status: Completed
• Phase: N/A
• Start date: July 1, 2019
• Est. completion date: November 16, 2019

Study information

• Verified date: November 2019
• Source: Tokyo University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Description:

Prior to Study: Total 500: Screen potential subjects by inclusion and exclusion criteria; obtain endoscopy images.

Randomization was performed.

Intervention: AI diagnosis was performed for 250 patients using upper gastrointestinal endoscopy images, and Expert endoscopists diagnosis was performed for 250 patients by same methods.

Primary analysis: Perform primary analysis of primary and secondary endpoints for 250 patients in each group

Cross over diagnosis between AI and expert endoscopists was performed.

Perform secondary analysis of agreement of gastric cancer diagnosis per images and IOU between AI and expert endoscopists for 500 patients.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 500
• Est. completion date: November 16, 2019
• Est. primary completion date: October 1, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years and older

Eligibility

Inclusion Criteria:

1. Males or females aged = 20 years who underwent upper gastrointestinal endoscopy at Tokyo University Hospital during 2018.

2. Informed optout consent, obtained from each patient before completion of the study.

Exclusion Criteria:

1. Patients who underwent gastrectomy.

2. Patients who underwent transnasal upper gastrointestinal endoscopy.

Related Conditions & MeSH terms

• Gastric Cancer
• Stomach Neoplasms

Intervention

Diagnostic Test:
• AI-based diagnosis
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.
• The expert endoscopists-based diagnosis
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.

Locations

Country	Name	City	State
Japan	Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo	Tokyo

Sponsors (1)

Lead Sponsor	Collaborator
Tokyo University

Country where clinical trial is conducted

Japan, 

References & Publications (2)

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Per patient diagnosis of gastric cancer	Number of Participants	Up to 6 weeks from study start
Secondary	Number of images analyzed for diagnosis of gastric cancer	Number of upper gastrointestinal endoscopy images	Up to 6 weeks from study start
Secondary	Intersection over union (IOU) of gastric lesions	A value between 0 and 1	Up to 6 weeks from study start
Secondary	Diagnosis of advanced gastric cancer	Number of Participants diagnosed with advanced gastric cancer	Up to 6 weeks from study start
Secondary	Diagnosis of early gastric cancer	Number of Participants diagnosed with early gastric cancer	Up to 6 weeks from study start
Secondary	Agreement on image and IOU based diagnosis of gastric cancer between AI and expert endoscopists	Number of images and IOU value (between 0 and 1)	Up to 12 weeks from study start