AI trained to spot who will develop stomach cancer

NCT ID NCT04840056

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Recruitment status, easiest to join first

Recruiting now This study
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
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Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

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Status unknown
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First seen Sep 22, 2026 · Last updated Sep 22, 2026

Summary

Researchers are building a machine learning tool to predict which people with intestinal metaplasia or atrophic gastritis will develop gastric cancer. The study includes about 1,300 adults with these stomach conditions, confirmed by tissue samples. The team will combine clinical information and microscopic tissue features to train the algorithm and test how well it identifies future cancer risk.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
a machine learning algorithm that predicts gastric cancer risk from clinical and tissue data
What this could lead to
If it works, doctors could use this tool to flag which patients with intestinal metaplasia or atrophic gastritis face the highest risk of gastric cancer and need closer monitoring.
What could go wrong
The algorithm may not predict cancer accurately enough to be useful, and its performance in this group of patients may not carry over to other populations. It also needs validation before it can guide care.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 1,300 people

The number the study aims to enrol. It can still change while the study runs.

Started

Apr 2021

Expected to finish

Dec 2026

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

Clinical data will be collected retrospectively using the Clinical Data Analysis and Reporting System (CDARS) and Clinical management System (CMS). A cluster-wide cohort (New Territories East Cluster, NTEC) consisting of patients with history of histologically-proven gastric IM and AG will be identified and included for subsequent analysis. The data collection period for the retrospective data will be 2000-2020. Histology slides will be retrieved retrospectively when available (within NTEC). Whole slide imaging technique will be utilized for the development of training and validation cohorts with machine learning algorithms in the pathology part.

Ages

18 years and older

Sex

Anyone

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * Adults \>= 18 years of age * Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach) Exclusion Criteria: \- none

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Conditions

The condition(s) this trial relates to.

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

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  3. A doctor treating you

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Contacts and locations

Locations

  • Prince of Wales Hospital

    RECRUITING

    Shatin, New Territories, Hong Kong

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