Deep-learning tool to distinguish T1–T2 from T3 renal cell carcinoma on contrast-enhanced CT.
Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
This project will see if a computer model can tell whether kidney cancer is early (T1–T2) or locally advanced (T3) using contrast-enhanced CT scans before surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1000 (estimated) |
| Ages | 18 Years to 85 Years |
| Sex | All |
| Sponsor | Peking University First Hospital Academic / other |
| Locations | 1 site (Beijing) |
| Trial ID | NCT07166445 on ClinicalTrials.gov |
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.
Who should consider this trial
Good fit: Ideal participants are patients with histopathologically confirmed renal cell carcinoma who had preoperative contrast-enhanced CT performed at Peking University First Hospital with complete DICOM data and slice thickness ≤1 mm.
Not a fit: Patients with non‑RCC histology, CT scans with severe artifacts, scans performed elsewhere without compatible image quality, or without contrast-enhanced thin-slice CT are unlikely to benefit.
Why it matters
Potential benefit: If successful, the tool could reduce staging errors and help surgeons plan the most appropriate operation, potentially improving outcomes and avoiding under- or overtreatment.
How similar studies have performed: Prior small and single-center studies of CT-based deep learning for RCC staging have shown promising accuracy, but large independent validations and clinical integration remain limited.
Eligibility criteria
Show full inclusion / exclusion criteria
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 this trial is running
Beijing
- Peking University First Hospital, Beijing, — Beijing, China (Recruiting)
Study contacts
- Study coordinator: Zejin Ou
- Email: 2411210230@bjmu.edu.cn
- Phone: 159 1494 4390
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.