Clinical trial NCT04040374: Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer
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
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Gastric Cancer
Keywords: artificial intelligence
gastric cancer
Lead sponsor: Tokyo University
Sponsor class: OTHER
First posted: 2019-07-31
Enrollment: 500
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Per patient diagnosis of gastric cancer
timeFrame: Up to 6 weeks from study start
Secondary outcomes:
measure: Number of images analyzed for diagnosis of gastric cancer
timeFrame: Up to 6 weeks from study start
measure: Intersection over union (IOU) of gastric lesions
timeFrame: Up to 6 weeks from study start
measure: Diagnosis of advanced gastric cancer
timeFrame: Up to 6 weeks from study start
measure: Diagnosis of early gastric cancer
timeFrame: Up to 6 weeks from study start
measure: Agreement on image and IOU based diagnosis of gastric cancer between AI and expert endoscopists
timeFrame: Up to 12 weeks from study start
Other outcomes:
Locations:
city: Tokyo
country: Japan
facility: Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
Study officials:
affiliation: Tokyo University
name: Ryota Niikura, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
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.
citation: 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.