Deep-learning tool to distinguish T1–T2 from T3 renal cell carcinoma on contrast-enhanced CT.

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

Researchers will develop and validate a deep-learning model using preoperative contrast-enhanced CT scans linked to postoperative pathological stage from a single center. The model will be trained on annotated DICOM images with slice thickness ≤1 mm and evaluated on an independent test set to assess generalizability. Performance metrics will include AUC, sensitivity, specificity, positive and negative predictive values, and decision-curve analysis. The final goal is a decision-support tool that can be integrated into clinical PACS to inform preoperative staging and surgical planning.

Why it matters

Who can join: the full rules

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.

Where it's running

• Peking University First Hospital, Beijing,
  Beijing, China Recruiting

Study contacts

• Study coordinator: Zejin Ou
• Email: 2411210230@bjmu.edu.cn
• Phone: 159 1494 4390

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.