Gastric CancerGAINNCT06971471
Artificial Intelligence to Solve the MissINg of Gastric Cancer (AIMING)
A study testing Integration of Artificial Intelligence (AI) assistance to screening gastroscopy in people with Gastric Cancer, run by Istituto Clinico Humanitas.
Trial summary
Our AIMING 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. Read more about this study
- Who is it for?
- Ages 60 years and older
- Conditions the study is for
- Gastric Cancer
- What is being tested?
- Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 6,600 participants
- Where
- No sites listed yet
- Study dates
- 10 Jun 2025to Jun 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 60 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All >60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications at high-risk of gastric cancer..
Who cannot join
- 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.
About this study
Official title: Gastric Cancer and Artificial Intelligence: a National-level Project
Our AIMING 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).
- Conditions
- Gastric Cancer
- Being tested
- Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Integration of Artificial Intelligence (AI) assistance to screening gastroscopyDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Parallel arm 1 | No intervention | 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 |
| Parallel arm 2 | Active comparator | 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. Receives: Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy |
| Cross-over arm 1 (control) | Other | 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. Receives: Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy |
| Cross-over arm 2 | Active comparator | 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. Receives: Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Miss rate reduction
change of the miss rate of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (tandem).
Measured: 2025: 12 months enrollment
Secondary outcomes (2)
Change number of Detections
Change in the detection of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (parallel).
Measured: 1 day procedure and follow up for 2 years
patient satisfaction
Assessment of patient acceptability, satisfaction and tolerance, assessed by questionnaire, towards AI technology for both the detection and the characterization of gastric lesions.
Measured: 2025: during the 12 months enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 May 2025
- Study start10 Jun 2025 (estimated)
- Primary completionJun 2027 (estimated)
- Study completionJun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric CancerStudies of the same condition, open and under way.
- More trials run by Istituto Clinico HumanitasThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Parallel/Crossover Study Model; Patients will be randomized 1:1:1:1
- Masking
- None (open label)
- Enrolment
- 6,600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istituto Clinico Humanitas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06971471
- Sponsor's protocol ID
- AIMING
- Acronym
- GAIN
- Status last verified
- May 2025
- First submitted
- 6 May 2025
- Last updated
- 14 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Gastric Cancer and Artificial Intelligence: a National-level Project
- ClinicalTrials.gov ID
- NCT06971471
- Acronym / sponsor ID
- GAIN / AIMING
- Conditions
- Gastric Cancer
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 6,600 participants (estimated)
- Lead sponsor
- Istituto Clinico HumanitasUniversity, hospital or other
- Study start
- 10 Jun 2025 (estimated)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Jun 2028 (estimated)
- First posted
- 14 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Our AIMING 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 design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 4
- Model description
- Parallel/Crossover Study Model; Patients will be randomized 1:1:1:1
| Arm | Type | Receives |
|---|---|---|
| Parallel arm 1 | No intervention | –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 |
| Parallel arm 2 | Active comparator | Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopypatients 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. |
| Cross-over arm 1 (control) | Other | Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopypatients 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. |
| Cross-over arm 2 | Active comparator | Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopypatients 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. |
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All >60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications at high-risk of gastric cancer..
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Integration of Artificial Intelligence (AI) assistance to screening gastroscopyTwo 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. | Device | Cross-over arm 1 (control), Cross-over arm 2, Parallel arm 2 |
Primary outcome
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).
2 secondary outcomes
Change number of Detections
Time frame: 1 day procedure and follow up for 2 years
patient satisfaction
Time frame: 2025: during the 12 months enrollment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 May 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Integration of Artificial Intelligence (AI) assistance to screening gastroscopy in people with Gastric Cancer, run by Istituto Clinico Humanitas.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 14 May 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Miss rate reduction (time frame: 2025: 12 months enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 May 2025.