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

Recruiting

• Conditions: Carcinoma, Renal Cell; Diagnostic Imaging; Pathology; Deep Learning

• Registration Number: NCT07166445
• Lead Sponsor: Peking University First Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-09-01
• Status Verified: 2025-08
• Study First Submitted: 2025-09-03
• Study First Submitted QC: 2025-09-03
• Last Update Submitted: 2025-09-03
• Study First Posted: 2025-09-10
• Last Update Posted: 2025-09-10
• Primary Completion: 2025-12-01
• Study Completion: 2027-12-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
diagnostic performance	from 2024 to 2027

Trial Locations

Locations (1)

Peking University First Hospital, Beijing,; 🇨🇳 Beijing, China