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RECRUITINGNCT07166445

Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT

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 quantifyin...

Lead sponsor: Peking University First Hospital•1 locations•View source on ClinicalTrials.gov

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Contacts

Zejin Ou

CONTACT

159 1494 4390 2411210230@bjmu.edu.cn

Sponsors

Lead Sponsor

Peking University First Hospital

Age

18 - 85 years

Sex

ALL

Healthy Volunteers

Yes

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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Data from ClinicalTrials.govLast updated on ClinicalTrials.gov: September 10, 2025Terms

Trial snapshot

StatusRECRUITING

Phase—

Enrollment1,000

Start dateSep 2024

Last updatedSep 10, 2025

Condition

Carcinoma, Renal Cell Diagnostic Imaging Pathology Deep Learning

Locations shown

Peking University First Hospital, Beijing,

Beijing

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Data Source & Attribution

This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

ClinicalTrials.gov last update: September 10, 2025Data synced to Clareo: May 17, 2026

Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.

Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.

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