The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study

Summary

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

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

Conditions2

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