What question did this study set out to answer?

The aim is to develop a machine learning model to predict surgical outcomes for renal hilar tumors using an enhanced nephrometry scoring system.

January 25, 2026

Development and Validation of Predictive Models for Trifecta Achievement in Robot-Assisted Partial Nephrectomy for Renal Hilar Tumors: Preliminary Application of the Modified R.E.N.A.L. Score

Key Points

The aim is to develop a machine learning model to predict surgical outcomes for renal hilar tumors using an enhanced nephrometry scoring system.
Analyzed data from 414 patients undergoing robot-assisted partial nephrectomy.
Applied least absolute shrinkage and selection operator regression and Boruta algorithm.
Developed and evaluated five machine learning models using various statistical techniques.
Validated model performance with external patient cohort and SHAP for interpretability.
Created a modified nephrometry score based on logistic regression findings.
168 out of 304 patients achieved trifecta outcomes.
Logistic regression identified eight significant predictive variables.
The machine learning model showed strong performance in both internal and external validation.
Surgeon, tumor classification, and tumor radius were key predictors identified by SHAP.
Modified R.E.N.A.L. score outperformed traditional scoring in assessing operation time and kidney function.

Abstract

Objective: To develop an interpretable machine learning (ML) model for predicting surgical outcomes in renal hilar tumors and propose a hilar-specific anatomical nephrometry scoring system. Methods: A total of 414 patients with renal hilar tumors who underwent robot-assisted partial nephrectomy (RAPN) were included in this study, comprising 304 patients from the First Affiliated Hospital of Nanchang University and 110 patients from the Second Affiliated Hospital of Nanchang University, which served as the external validation cohort. To identify predictors of trifecta achievement, we used least absolute shrinkage and selection operator regression and the Boruta algorithm, followed by multivariate logistic regression to identify independent factors. Five ML models were developed and evaluated using receiver operating characteristic curves, calibration plots, decision curve analysis, and precision–recall curves. The generalizability of the model was further validated in external cohort. Finally, we used SHapley Additive exPlanations (SHAP) to interpret the contribution of each predictor and enhance the model’s explainability. Furthermore, based on anatomical features identified through logistic regression, we developed a modified nephrometry scoring system and compared its risk stratification performance with the traditional R.E.N.A.L. (i.e., Radius, Exophytic or endophytic, Nearness, Anterior or posterior, and Location) scoring system. Results: Among the 304 patients in the primary cohort, 168 achieved trifecta outcomes. Eight variables were incorporated into the predictive model, with logistic regression model ultimately being selected as the optimal predictive model. It showed robust predictive performance in internal and external validation. SHAP methods identified surgeon, classification of hilar tumor, and radius as the three most significant predictive variables. Compared with the traditional R.E.N.A.L. score, the modified R.E.N.A.L. score demonstrated superior stratification ability for operation time, change in serum creatinine, change in estimated glomerular filtration rate, and trifecta achievement. Conclusion: The interpretable ML model accurately predicts trifecta in RAPN for hilar tumors. The modified R.E.N.A.L. score provides refined anatomical stratification and facilitates individualized surgical planning.

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