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Artificial intelligence reads tissue samples in minutes to guide cancer surgery

NCT ID NCT07708207

Knowledge-focused Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University 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 This study
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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 Jul 16, 2026 · Last updated Jul 17, 2026 · Updated 1 time

Summary

This study is developing an artificial intelligence model that can analyze frozen-section pathology images—tissue samples examined during surgery. The goal is to help pathologists quickly and accurately diagnose cancer while the patient is still in the operating room. Researchers will train the AI on thousands of past cases and then test it in real-world hospital settings across multiple organ systems.

What this could mean

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

Active substance
artificial intelligence foundation model for frozen-section pathology
What this could lead to
If successful, this AI model could help pathologists diagnose cancer faster and more accurately during surgery, potentially improving patient outcomes.
What could go wrong
This is an observational study, so the AI is not yet tested in real-time decision-making. Its performance may vary across different hospitals or patient groups.

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 33,000 people

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

Started

Apr 2026

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

The study population includes patients undergoing intraoperative frozen-section pathological examination at participating tertiary hospitals in China. The study will include patients with benign or malignant diseases involving various organs. Both retrospective and prospective cohorts will be included.

Ages

Children (under 18), adults (18 to 64) and older adults (65 and over)

Sex

Anyone

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: * Patients undergoing surgery with intraoperative frozen-section pathological examination. * Availability of complete clinical information and intraoperative frozen-section pathology records. * Availability of digitized frozen-section whole-slide images suitable for artificial intelligence analysis. Exclusion Criteria: * Frozen-section whole-slide images with inadequate quality for evaluation, including substantial blur, ghosting, severe artifacts, or insufficient diagnostic tissue. * Missing or indeterminate key clinical, intraoperative pathology, or pathological reference data required for the prespecified study task. * Withdrawal of informed consent in the prospective validation cohort, where applicable.

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Conditions

The condition(s) this trial relates to.

cancer neoplasm

As listed by the trial registrant

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

Artificial intelligence (AI) Cancer Intraoperative pathology

Contacts and locations

Locations

  • Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong

    Guangzhou, China

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