Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
Condition(s) studied
Investigational drug(s) / intervention(s)
None intervention: this study is retrospective based on the CT images, which dose include any intervention.
Study 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.
Eligibility
Primary outcome measure(s)
- diagnostic performance — from 2024 to 2027
Trial sites (1)
| Facility | City | Region | Status |
|---|---|---|---|
| Peking University First Hospital, Beijing, | Beijing | China | Recruiting |
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Other trials for the same condition
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT07166445 on ClinicalTrials.gov ↗ ← All trials in China