Predicting disease-free survival in kidney cancer using deep learning and CT scans
Official title: Urology Department of the First Affiliated Hospital of Chongqing Medical University
Run by First Affiliated Hospital of Chongqing Medical University · NCT06088134 · Data updated 2026-11-12
This study is testing a new computer model that uses CT scans to see if it can help predict how long patients with localized kidney cancer will be free of disease after surgery.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 800 (estimated) |
| Sponsor | First Affiliated Hospital of Chongqing Medical University Academic / other |
| Locations | 1 site (Chongqing, Chongqing Municipality) |
| Status | Recruiting |
| Started | September 1, 2022 |
| Main results expected | June 1, 2025 |
| Study ends | August 1, 2025 |
| Registry record updated | May 31, 2025 |
| Trial ID | NCT06088134 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are patients who have undergone partial or radical nephrectomies and have a histological diagnosis of localized ccRCC with complete clinical and preoperative CT image data.
✕Probably not a fit
Patients who may not benefit from this study include those with incomplete clinical data, unsuitable CT images, or those who have received neoadjuvant or adjuvant therapies.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study aims to develop a deep learning model that utilizes contrast-enhanced CT images to predict disease-free survival (DFS) in patients with localized clear cell renal cell carcinoma (ccRCC) before surgery. The model will be validated using data from multiple centers and its predictive accuracy will be compared to traditional prognostic models. By leveraging advanced imaging techniques and machine learning, the study seeks to enhance preoperative decision-making for patients diagnosed with ccRCC.
Why it matters
Potential benefit: If successful, this approach could provide more accurate predictions of disease-free survival, helping to tailor treatment plans for patients with localized ccRCC.
How similar studies have gone: Other studies utilizing deep learning models for cancer prognosis have shown promising results, indicating potential success for this approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * underwent partial/radical nephrectomies * histologically diagnosed as ccRCC * with complete clinical data and preoperative CT image data Exclusion Criteria: * with incomplete clinic-pathological data * lack of preoperative contrast-enhanced CT images or the image quality was unsuitable for analysis * who received pre-surgery neoadjuvant or adjuvant therapies * with multiple renal tumors or/and had synchronous metastasis
Where it's running
-
Yingjie Xv
Chongqing, Chongqing Municipality, China Recruiting
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.