Actively Recruiting
Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
Led by Peking University First Hospital · Updated on 2025-09-10
1000
Participants Needed
1
Research Sites
169 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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
Official Title
Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
Who Can Participate
Eligibility Criteria
You may qualify if you...
- 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 will not qualify if you...
- Pathologic subtype other than renal cell carcinoma.
- Images with severe artifacts.
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Peking University First Hospital, Beijing,
Beijing, China
Actively Recruiting
Research Team
Z
Zejin Ou
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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