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

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.

NCT Number	NCT07166445
Lead Sponsor	Peking University First Hospital
Conditions	Carcinoma, Renal Cell, Diagnostic Imaging, Pathology, Deep Learning
Enrollment	1,000 participants
Start Date	2024-09-01
Primary Completion	2025-12-01 (estimated)
Study Completion	2027-12-01 (estimated)
Updated on ClinicalTrials.gov	2025-09-10