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

NCT07166445 | Recruiting DMC

• 1 other identifier: PUH-2025-RCC-DL-TS001
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief 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. 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

Outcome Measures

Primary Outcomes (1)

• diagnostic performance

  from 2024 to 2027

Interventions

None intervention OTHER

this study is retrospective based on the CT images, which dose include any intervention.

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• \. Pathologic subtype other than RCC. 2. Images with severe artifacts.

Sponsors & Collaborators

• Peking University First Hospital: lead

Study Sites (1)

Peking University First Hospital, Beijing,

Beijing, China

RECRUITING

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Zejin Ou

CONTACT

159 1494 4390; 2411210230@bjmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 3, 2025
• First Posted: September 10, 2025
• Study Start: September 1, 2024
• Primary Completion: December 1, 2025
• Study Completion (Estimated): December 1, 2027
• Last Updated: September 10, 2025

Record last verified: 2025-08

Locations

CN (1)