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AI could help surgeons spot aggressive kidney tumors before operating

NCT ID NCT07166445

Diagnosis Sponsor: Peking University First Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

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.
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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 26, 2026

Summary

This study is testing whether a computer program (deep learning) can accurately tell the difference between early-stage (T1-T2) and more advanced (T3) kidney cancer on CT scans. Researchers will train the AI on 1,000 patients' scans and check its accuracy. If it works, the tool could be added to hospital systems to help surgeons plan the best treatment.

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 tool could help doctors more accurately stage kidney cancer before surgery, leading to better treatment decisions and fewer unnecessary operations.
What could go wrong
This is a retrospective study using existing images, not a real-world test. The AI may not perform as well in different hospitals or with different CT machines.

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 1,000 people

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

Started

Sep 2024

Expected to finish

Dec 2027

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

Patients who underwent surgery at the Department of Urology, Peking University First Hospital, with postoperative pathological confirmation of renal cell carcinoma (RCC), and who also have complete preoperative contrast-enhanced CT datasets (slice thickness ≤1 mm, lossless DICOM) and definitive pathological staging of pT1a-T2b or pT3a.

Ages

18 to 85 years

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

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. Histopathologically confirmed renal cell carcinoma on postoperative specimen. 2. Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets. 3. Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a. 4. CT image quality deemed adequate for analysis. Exclusion Criteria: * 1\. Pathologic subtype other than RCC. 2. Images with severe artifacts.

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Conditions

The condition(s) this trial relates to.

renal cell adenocarcinoma renal cell carcinoma

As listed by the trial registrant

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

Carcinoma, renal cell Deep learning Diagnostic imaging Pathology

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.

    Open the record ↗

  3. 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.

Contacts and locations

Locations

  • Peking University First Hospital, Beijing,

    RECRUITING

    Beijing, China

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