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AI reads brain tumor types from routine slides, no special stains needed

NCT ID NCT07685301

Knowledge-focused Sponsor: Huashan Hospital 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 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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 Jul 06, 2026 · Last updated Jul 07, 2026 · Updated 1 time

Summary

This study develops an artificial intelligence model that can classify brain tumors into categories, families, and specific diagnoses using only standard stained tissue slides. Researchers will train the AI on 20,000 archived images from two hospitals and test its accuracy against known diagnoses. If it works, the AI could speed up diagnosis and reduce reliance on expensive molecular testing.

What this could mean

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

What this could lead to
If successful, this AI model could help pathologists diagnose brain tumors faster and more accurately from routine slides, potentially reducing the need for additional molecular tests.
What could go wrong
This is a retrospective observational study using existing data, so the AI's performance in real-time clinical use is unknown. The model may not generalize well to other hospitals or patient populations.

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 20,000 people

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

Expected to start

Aug 2026

An estimate. Start dates often move.

Expected to finish

Jul 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.

Who is studied

The study population consists of pediatric (≥9) and adult patients of any sex who underwent neurosurgical resection or biopsy for a suspected central nervous system (CNS) tumor at Huashan Hospital, Fudan University, between January 1, 2010 and December 31, 2025, and who have an available postoperative pathological diagnosis, archived hematoxylin and eosin (H\&E) stained slides and/or digital whole-slide images, and sufficient linked de-identified clinical, pathological, and molecular data for retrospective analysis. The cohort includes patients with primary or secondary CNS tumors for whom routine clinical care generated pathology materials suitable for computational pathology analysis.

Ages

9 years and older

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: 1. Patients who underwent brain or spinal tumor resection or biopsy at Huashan Hospital Fudan University and Shandong Provincial Hospital. 2. Postoperative pathology diagnosis consistent with a primary or secondary central nervous system tumor. 3. Availability of archived routine H\&E-stained glass slides or existing digital whole-slide image files of adequate quality for analysis. 4. Availability of essential de-identified clinical and pathological information, including age, sex, tumor location, and key surgical/pathology records. 5. Use of archived data and samples permitted under institutional ethics approval, including waiver of informed consent where applicable. Exclusion Criteria: 1. Severe slide preparation or scanning artifacts that preclude meaningful computational analysis, including extensive tissue folding, severe bubbles, severe detachment, markedly uneven staining/fading, or severe out-of-focus scanning. 2. Insufficient viable tumor tissue or insufficient analyzable tumor area for patch extraction. 3. Missing or uncertain pathological diagnosis that cannot be reliably reassigned according to the WHO 2021 CNS tumor classification using available records. 4. Cases lacking sufficient clinical, pathological, or molecular information required for core study analyses. 5. Other cases determined by the investigators to be unsuitable for algorithm training or evaluation after quality control review.

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Conditions

The condition(s) this trial relates to.

brain cancer brain neoplasm central nervous system cancer central nervous system neoplasm

As listed by the trial registrant

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

Brain tumors Central nervous system neoplasms

How to take part

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  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

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