The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study
The goal of this observational study is to develop a CNN-based machine module to predict postoperative fractional renal function in people who are proposed to undergo partial nephrectomy. The main question it aims to answer is:
• Does this machine learning model accurately predict renal function after partial nephrectomy?
Eligibility
Sex
ALL
Min age
18 Years
Max age
80 Years
Healthy volunteers
No
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
Primary outcome measure(s)
GFR of ipsilateral and contralateral kidneys — 3 months after surgery
volume of ipsilateral kidney — 3 months after surgery
Trial sites (2)
Facility
City
Region
Status
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing
Jiangsu
Recruiting
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
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.
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