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

Recruitingn/aAges 18 Years - 85 YearsAllPeking University First Hospital
NCT07166445 on ClinicalTrials.gov ↗
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In plain words

What the trial is, and whether it could suit you

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.

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📋 Eligibility rules (as published)
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.
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What it tests

  • None intervention
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Where

1 site
Peking University First Hospital, Beijing,
Beijing, China
🤝 Taking part is your choice, and you can leave at any time. Only the trial team can confirm you are eligible — always decide together with your own doctor.
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