Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
Recruiting
Observational Study
Carcinoma, Renal Cell
Diagnostic Imaging
Pathology
Deep Learning
Healthy Volunteers Welcome
You do not need to have the condition being studied to take part.
No Study Drug
Researchers observe your health over time — no experimental treatment is given.
At a Glance
- Age
- 18 – 85
- Sex
- Any
- Study type
- Observational
- Participants needed
- 1,000 (estimated)
- Sponsor
- Peking University First Hospital · Other
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About This Trial
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-suppo…
Trial Locations
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Eligibility Criteria
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.…
Contacts