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AI reads breast scans to spot cancer better

NCT ID NCT07605195

Diagnosis Sponsor: Yunnan Cancer Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting This study
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
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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 Jun 26, 2026 · Last updated Jun 27, 2026 · Updated 1 time

Summary

This study tests an artificial intelligence (AI) system that analyzes breast images (digital breast tomosynthesis) to help doctors diagnose breast cancer more accurately. Researchers will train the AI on thousands of images from 5000 women to detect tumors, tell benign from malignant, and predict cancer types. The goal is to improve diagnosis, especially for hard-to-classify cases, and guide treatment decisions.

What this could mean

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

Active substance
deep learning AI model
What this could lead to
If successful, this could lead to a more accurate, AI-assisted tool for diagnosing breast cancer from imaging, reducing unnecessary biopsies.
What could go wrong
This is an early-stage study without a treatment intervention, so it may not improve patient outcomes in practice. The AI model might not work as well in real-world settings.

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.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

About 5,000 people

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

Expected to start

May 2026

An estimate. Start dates often move.

Expected to finish

Jun 2029

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.

Ages

18 to 80 years

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 1. Female patients aged ≥ 18 years. 2. Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views. 3. Confirmed pathological diagnosis (core needle biopsy or surgical resection) serving as the reference standard; or benign lesions with stable findings on follow-up for more than 2 years. 4. (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results. 2\. Exclusion Criteria 1. Poor image quality with severe artifacts that precluded reliable analysis. 2. History of previous breast surgery or radiotherapy (except for the recurrence risk subgroup). 3. Incomplete clinical or pathological data.

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Conditions

The condition(s) this trial relates to.

breast carcinoma breast neoplasm

As listed by the trial registrant

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

Breast carcinoma

How to take part

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

  1. The official record

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

    Open the record ↗

  2. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

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