NCT06751498
The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study
Recruiting · Not specified · Shao Pengfei · registry updated 2025-04-17
Inclusion and exclusion lines below are quoted from ClinicalTrials.gov. No match score is shown, because a score needs a person's age, biomarkers, and treatment dates. Confirm the record with the study team.
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?
Inclusion
- 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
- 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