GAIN Project: Gastric Cancer and Artificial Intelligence (GAIN)

June 3, 2024 updated by: Istituto Clinico Humanitas

Gastric Cancer and Artificial Intelligence: a National-level Project

Our GAIN project comprises four core work packages (WPs): WP1. Nation-level randomized controlled trial; WP2. Development of an innovative AI tool; WP3. Novel microsimulation modelling; WP4. Patient inclusion.

The nation-level multi-center tandem randomized controlled trial (WP1) will contribute to a better understanding of how the real-time AI algorithm can reduce miss rate of early gastric cancer and dysplasia during gastroscopy. Moreover, the innovation project will contribute to development of a novel AI tool (WP2) that can stratify the risk of gastric cancer by identifying in vivo precancerous conditions. Furthermore, a microsimulation modelling will allow us to predict how the use of AI can prevent gastric cancer and affect cost and patients' burdens. The assessment of the balance between benefits and harms is quite crucial especially for this type of medical device because the value of innovative tools is sometimes overestimated due to stakeholders' enthusiasm (WP3). Finally, we will take care of patients' perspective throughout the study project by including patient organization in both WP1, 2, and 3 (WP4).

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

6600

Phase

  • Not Applicable

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • All >60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications in Italian areas at high-risk of gastric cancer (Lombardia, Emilia Romagna, Veneto, Friuli-Venezia Giulia).

Exclusion Criteria:

  • contraindications to upper-GI endoscopy.
  • contraindications to biopsy.
  • active upper-GI bleeding or urgent upper-GI endoscopy.
  • patients with previous upper-GI surgery involving the stomach.
  • patients who were not able or refused to give informed written consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Parallel arm 1
patients will undergo standard high-definition and high-quality upper-GI endoscopy for the detection of gastric lesions with histological mapping according to Sydney system
Active Comparator: Parallel arm 2
patients will undergo high-definition and high quality upper-GI endoscopy with real-time assistance by real-time artificial intelligence for the detection of early gastric cancer and gastric dysplasia.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1.

The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.
Other: Cross-over arm 1 (control)
patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be without Artificial Intelligence assistance, and the second with Artificial Intelligence in order to define the miss rate for standard unassisted upper-GI endoscopy.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1.

The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.
Active Comparator: Cross-over arm 2
patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be with Artificial Intelligence assistance, and the second without Artificial Intelligence in order to define the decrease of miss rate when assistance by Artificial Intelligence is implemented.
Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1.

The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Miss rate reduction
Time Frame: 2025: 12 months enrollment
change of the miss rate of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (tandem).
2025: 12 months enrollment

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change number of Detections
Time Frame: 1 day procedure and follow up for 2 years
Change in the detection of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (parallel).
1 day procedure and follow up for 2 years
patient satisfaction
Time Frame: 2025: during the 12 months enrollment
Assessment of patient acceptability, satisfaction and tolerance, assessed by questionnaire, towards AI technology for both the detection and the characterization of gastric lesions.
2025: during the 12 months enrollment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Istituto Clinico Humanitas

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 10, 2024

Primary Completion (Estimated)

June 1, 2026

Study Completion (Estimated)

June 1, 2028

Study Registration Dates

First Submitted

February 2, 2024

First Submitted That Met QC Criteria

February 16, 2024

First Posted (Actual)

February 23, 2024

Study Record Updates

Last Update Posted (Estimated)

June 4, 2024

Last Update Submitted That Met QC Criteria

June 3, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GAIN

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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