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This is a Quantitative Study study.

September 27, 2025BJUI Compass0 citationsOpen Access

AI‐driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models

JMJulia Mühlbauer LGLuise Ingvelde Monika Gottstein LELuisa Egen

Key Points

Neural networks achieved the highest AUROC for predicting major complications at 0.762, indicating effective risk assessment.
For acute kidney injuries, the neural network model performed best with an AUROC of 0.717, supporting its predictive capabilities.
Machine learning models were developed from a dataset of 963 patients, enhancing the precision of preoperative risk stratification.
The study highlights the potential for machine learning to outperform traditional models, suggesting better patient monitoring post-surgery.

Abstract

Abstract Background and Objective Preoperative risk stratification in renal tumour surgery is essential to enable risk‐adjusted postoperative patient monitoring. Machine learning (ML) models predicting major complications (MCs) and acute kidney injuries (AKIs) following partial (PN) or radical nephrectomy (RN) have not been made, nor have they been compared with traditional logistic regression models. Design, setting and participants A total of 963 patients who underwent PN and RN between January 2017 and March 2023 at the University Medical Center Mannheim were included. The dataset consisted of 30 variables of interest– 18 descriptive and 12 predictor variables, which allowed for 7 predictor variables per event. The dataset was pre‐processed, and ML models were created for MC and AKI. The selected models included Random Forest (RF), Support Vector Machines (SVMs), Stochastic Gradient Boosting, Neural Networks (NNs) and Elastic Net Logistic Regression models (ENETs). Results and limitations For major complications, the NN model had the best model fitting, with an AUROC of 0.762 95%CI 0.611–0.912, a sensitivity of 0.86 95%CI 0.80–0.92 and a Brier score of 0.17 95%CI 0.11–0.23. For AKI, the best fit model was created using a NN with an AUROC of 0.717 95%CI 0.611–0.823, a sensitivity of 0.82 95%CI 0.74–0.90 and a Brier score of 0.24 95%CI 0.17–0.31. The best performing models for both outcomes outperformed the ENETs. Conclusions The ML models provide valuable information for preoperative risk stratification of patients undergoing renal tumour surgery. This study suggests that NNs are the most appropriate models to stratify patients regarding the occurrence of MCs and AKIs, respectively. The models are made publicly available for reproducibility.

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