Using AI to predict kidney function after partial kidney removal
Official title: The Value of a Renal Artery Perfusion Model Based on Convolutional Neural Network in Predicting Renal Function After Partial Nephrectomy: A Prospective, Single-Center Study
Run by The First Affiliated Hospital with Nanjing Medical University · NCT06751498 · Data updated 2026-11-12
This study is testing if a new AI tool can accurately predict how well your kidneys will work after you have part of one removed due to cancer.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 80 Years |
| Sex | All |
| Enrollment | 300 (estimated) |
| Sponsor | The First Affiliated Hospital with Nanjing Medical University Academic / other |
| Drugs / interventions | chemotherapy, immunotherapy |
| Locations | 2 sites (Nanjing, Jiangsu and 1 other locations) |
| Status | Recruiting |
| Started | January 1, 2025 |
| Main results expected | April 1, 2027 |
| Study ends | January 1, 2028 |
| Registry record updated | April 17, 2025 |
| Trial ID | NCT06751498 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates include individuals with stage cT1 renal tumors who are scheduled for partial nephrectomy and have a good performance status.
✕Probably not a fit
Patients with unresectable lesions, abnormal preoperative renal function, or those receiving certain preoperative therapies may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study aims to develop a convolutional neural network (CNN)-based model to predict postoperative fractional renal function in patients scheduled for partial nephrectomy due to renal cell cancer. The study will assess the accuracy of this machine learning model by comparing its predictions with actual renal function measurements obtained through nuclear renal function tests, eGFR, and CTA. By evaluating the feasibility of this predictive model, the study seeks to enhance the preoperative diagnostic and treatment processes for renal tumors, focusing on functional outcomes.
Why it matters
Potential benefit: If successful, this study could provide a reliable method for predicting renal function after surgery, potentially improving patient outcomes and surgical decision-making.
How similar studies have gone: While the use of AI in medical predictions is gaining traction, this specific application of a CNN-based model for renal function prediction after partial nephrectomy is relatively novel and has not been extensively tested in prior studies.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * people with stage cT1 renal tumors confirmed by preoperative CT or MR * people who are proposed to undergoing partial nephrectomy * localized renal tumors without lymph node and distant metastases as defined by NCCN guidelines * ECOG score of 0 or 1 * Life expectancy greater than 10 years Exclusion Criteria: * people with surgically unresectable lesions * people with Abnormal preoperative renal function, eGFR(estimated by CKD-EPI)\<90ml/min/1.73m2 * people who receive preoperative molecular targeted therapy, immunotherapy, chemotherapy * people with any contraindications to surgery * people who convert to radical nephrectomy during surgery * people who receive molecular targeted therapy, immunotherapy or chemotherapy during the postoperative follow-up period * people with serious systemic disease
Where it's running
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The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, China Recruiting -
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, China Not yet recruiting
Study contacts
- Study coordinator: Shao Pengfei, Professor
- Email: spf032@hotmail.com
- Phone: +8613851925825
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
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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.