This study presents a machine learning model to predict renal function decline following minimally-invasive partial nephrectomy. Using a dataset of 556 patients treated between 2015 and 2023, the model incorporated patient, tumor, and intraoperative surgical variables - including clamping strategy, resection technique, and renorrhaphy type - to estimate the 3-month postoperative eGFR drop.
A Random Forest Regressor outperformed other models, achieving a prediction accuracy of 89.29%, a mean absolute error of 8.09 mL/min/1.73 m2, and a strong correlation with observed outcomes (r=0.904, P<10-42). These findings support the use of AI for personalized surgical planning and functional outcome prediction in nephron-sparing surgery.
Minerva urology and nephrology. 2025 Jun [Epub]
Daniele Amparore, Alberto Piana, Andrea Simeri, Vincenzo Pezzi, Michele DI Dio, Cristian Fiori, Gianluigi Greco, Francesco Porpiglia
Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy - ., Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy., Department of Mathematics and Computer Science, University of Calabria, Rende, Cosenza, Italy., Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Rende, Cosenza, Italy., Department of Surgery, Division of Urology, SS Annunziata Hospital, Cosenza, Italy.
PubMed http://www.ncbi.nlm.nih.gov/pubmed/40528776