Carcinoma, Renal CellNCT07166445
Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
An observational study of Carcinoma, Renal Cell and Diagnostic Imaging, run by Peking University First Hospital at 1 site in China.
Trial summary
This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. Read more about this study
- Who is it for?
- Ages 18 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Carcinoma, Renal Cell, Diagnostic Imaging, Pathology
- What is being tested?
- None intervention
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2024to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 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.
Who may be able to join
- Histopathologically confirmed renal cell carcinoma on postoperative specimen.
- Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets.
- Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a.
- CT image quality deemed adequate for analysis.
Who cannot join
- 1. Pathologic subtype other than RCC. 2. Images with severe artifacts.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Peking University First Hospital, Beijing,
Beijing
Peking University First Hospital Peking University First Hospital, 2411210230@bjmu.edu.cn
About this study
This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. By quantifying the model's diagnostic performance on an independent test set-using AUC, sensitivity, specificity, positive/negative predictive values, and decision-curve analysis-we will establish a decision-support tool that can be seamlessly integrated into clinical PACS, thereby reducing staging errors, refining surgical planning, and improving patient outcomes.
- Conditions
- Carcinoma, Renal Cell, Diagnostic Imaging, Pathology, Deep Learning
- Being tested
- None intervention
- Medical subject headings
- Carcinoma, Renal Cell
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
None interventionOther
this study is retrospective based on the CT images, which dose include any intervention.
What is being measured
The primary outcome is the main question the study is designed to answer.
diagnostic performance
Measured: from 2024 to 2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Sep 2025
- Study start1 Sep 2024
- Primary completion1 Dec 2025 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Carcinoma, Renal CellStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University First Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07166445
- Sponsor's protocol ID
- PUH-2025-RCC-DL-TS001
- Status last verified
- Aug 2025
- First submitted
- 3 Sep 2025
- Last updated
- 10 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
- ClinicalTrials.gov ID
- NCT07166445
- Acronym / sponsor ID
- PUH-2025-RCC-DL-TS001
- Conditions
- Carcinoma, Renal Cell, Diagnostic Imaging, Pathology, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Peking University First HospitalUniversity, hospital or other
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 10 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. By quantifying the model's diagnostic performance on an independent test set-using AUC, sensitivity, specificity, positive/negative predictive values, and decision-curve analysis-we will establish a decision-support tool that can be seamlessly integrated into clinical PACS, thereby reducing staging errors, refining surgical planning, and improving patient outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- Histopathologically confirmed renal cell carcinoma on postoperative specimen.
- Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets.
- Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a.
- CT image quality deemed adequate for analysis.
Key exclusion criteria (1 listed)
- 1. Pathologic subtype other than RCC. 2. Images with severe artifacts.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| None interventionthis study is retrospective based on the CT images, which dose include any intervention. | Other | – |
Primary outcome
diagnostic performance
Time frame: from 2024 to 2027
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Carcinoma, Renal Cell and Diagnostic Imaging, run by Peking University First Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 10 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zejin Ou (159 1494 4390, 2411210230@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
diagnostic performance (time frame: from 2024 to 2027). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Sep 2025.