NCT07166445

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Peking University First Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 85 Years
• Healthy volunteers: Accepted

Summary

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.

Conditions

• Carcinoma, Renal Cell
• Diagnostic Imaging
• Pathology
• Deep Learning

Interventions

Type	Name	Description
OTHER	None intervention	this study is retrospective based on the CT images, which dose include any intervention.

Timeline

• Start date: 2024-09-01
• Primary completion: 2025-12-01
• Completion: 2027-12-01
• First posted: 2025-09-10
• Last updated: 2025-09-10

Locations

1 site across 1 country: China