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

Recruiting now

Conditions studied: Carcinoma, Renal Cell, Diagnostic Imaging, Pathology, Deep Learning

In brief

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.

Key facts

Study ID

NCT07166445

Run by

Peking University First Hospital

People needed

1000

Starts

2024-09-01

Expected to finish

2027-12-01

Last updated by the study team

2025-09-10

Who can join

Age: 18 and older, up to 85. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• 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 may not qualify if…

• 1. Pathologic subtype other than RCC. 2. Images with severe artifacts.

Where it is running

• Peking University First Hospital, Beijing, — Beijing, China (enrolling)

Full record on ClinicalTrials.gov

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