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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Per patient diagnosis of gastric cancer | Number of Participants | Up to 6 weeks from study start |
| Measure | Description | Time Frame |
|---|---|---|
| Number of images analyzed for diagnosis of gastric cancer | Number of upper gastrointestinal endoscopy images | Up to 6 weeks from study start |
| Intersection over union (IOU) of gastric lesions | A value between 0 and 1 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Ryota Niikura, MD | Tokyo University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo | Tokyo | 1138655 | Japan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25079317 | Background | 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. | |
| 29335825 | 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. |
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| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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|
| 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. |
|
| Up to 6 weeks from study start |
| Diagnosis of advanced gastric cancer | Number of Participants diagnosed with advanced gastric cancer | Up to 6 weeks from study start |
| Diagnosis of early gastric cancer | Number of Participants diagnosed with 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 | Number of images and IOU value (between 0 and 1) | Up to 12 weeks from study start |
| 34607377 | Derived | 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. |
| D004066 |
| Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |