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
104 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are developing and validating a deep-learning model based on contrast-enhanced CT scans to automatically distinguish between early-stage (T1-T2) and more advanced-stage (T3) renal cell carcinoma before surgery. This observational study aims to measure the accuracy of the model using various statistical tools including AUC, sensitivity, specificity, and predictive values. The goal is to create a decision-support tool that can be integrated into clinical imaging systems to reduce staging errors and improve surgical planning and patient outcomes. The study involves analyzing preoperative contrast-enhanced CT images collected retrospectively, without any intervention or treatment being administered. The CT scans used have a slice thickness of 1 mm or less and must meet quality standards suitable for analysis. Participants' postoperative specimens confirm the renal cell carcinoma diagnosis and staging, which serves as a reference for evaluating the model's diagnostic performance. Participants will have undergone preoperative CT imaging and surgery, with their data analyzed retrospectively during the study period from 2024 to 2027. Researchers will assess the diagnostic accuracy of the deep-learning model compared to the pathologic staging results. The study includes healthy volunteers and covers an age range from 18 to 85 years. There is no active treatment or follow-up involvement required from participants during the study.
CONDITIONS
Brief 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 the study 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
Your Study Journey
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Duration - From 2024 to 2027
Participants undergo retrospective analysis of preoperative contrast-enhanced CT images for automated discrimination between cancer stages.
1 visit (retrospective image review)
Trial Site Locations
Total: 1 location
1
Peking University First Hospital, Beijing,
Beijing, China
Actively Recruiting
Research Team
Z
Zejin Ou
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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