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AI trained to spot hidden clues in breast cancer slides

NCT ID NCT07842068

Diagnosis Sponsor: Hellenic Cooperative Oncology Group Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
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.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
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 Results may not be published yet.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

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Sep 25, 2026 · Last updated Sep 25, 2026

Summary

Researchers are testing whether deep learning algorithms can predict key breast cancer biomarkers, such as hormone receptors and HER2, by analyzing digitized tissue slides. The study uses stored tumor samples from about 3,600 patients with early breast cancer who received chemotherapy. The goal is to see if AI can match or improve on standard lab tests and reduce differences between pathologists.

What this could mean

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

Active substance
a deep learning algorithm that analyzes digitized tissue slides
What this could lead to
If it works, this could give pathologists a faster, more consistent way to read breast cancer biomarkers and match patients to the right treatments.
What could go wrong
The algorithm may not reliably detect biomarkers across different labs or patient groups, and it still needs validation before it can guide real treatment decisions.

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

3,648 people

The number who actually took part.

Started

Dec 1996

Finished

Jul 2026

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

patients with operable breast cancer treated with dds-CT including taxanes and anthracyclines. Adjuvant hormonal and radiation treatment were administered, as indicated

Ages

18 years and older

Sex

Female participants only

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

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: * Age 18 and above * Histologically confirmed BC * All treated with adjuvant dose-dense sequential chemotherapy (dds-CT) * Tumor tissue specimen (FFPE) availability Exclusion Criteria: * not adequate, and unsuitable tissue for IHC, FISH and analysis

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Conditions

The condition(s) this trial relates to.

breast cancer breast neoplasm

As listed by the trial registrant

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

Early breast cancer

More trials for these conditions

Other studies related to the condition(s) this trial covers.