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Updated: May 19, 2026

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Machine-Learning-Based Predictive Model for Trifecta Achievement in Robot-Assisted Partial Nephrectomy.

Boran Aksakal1, Tunkut Doganca2, Nicolas A Soputro3

  • 1School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.

Journal of Endourology
|May 18, 2026
PubMed
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This summary is machine-generated.

A new machine-learning nomogram accurately predicts trifecta achievement after robot-assisted partial nephrectomy (RAPN). This tool aids surgeons in decision-making and patient counseling for improved outcomes.

Area of Science:

  • Urology
  • Surgical Oncology
  • Machine Learning in Medicine

Background:

  • Robot-assisted partial nephrectomy (RAPN) is a standard treatment for kidney tumors.
  • Predicting trifecta achievement (negative margins, warm ischemia time ≤25 min, no complications) is crucial for surgical success.
  • Existing prediction models for RAPN outcomes are limited.

Purpose of the Study:

  • To develop and internally validate a machine-learning nomogram for predicting trifecta achievement in RAPN.
  • To identify key predictors of trifecta achievement in RAPN patients.
  • To provide a tool for individualized prediction and support clinical decision-making.

Main Methods:

  • Retrospective analysis of 426 patients undergoing RAPN.
  • Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression for variable selection.
Keywords:
machine learningnomogrampartial nephrectomyrenal tumorrobotic surgerysurgical outcome

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  • Development of a multivariate logistic regression nomogram, validated with internal bootstrap methods, discrimination, calibration, and decision-curve analysis (DCA).

    • Main Results:

    • Trifecta achievement rate was 87.7%.
    • Independent predictors included operative time, blood loss, tumor location, collecting system entry, and transfusion need.
    • The nomogram showed good discrimination (AUC=0.792) and acceptable calibration, with positive net benefit on DCA.

    Conclusions:

    • The developed LASSO-based nomogram provides individualized prediction of trifecta achievement in RAPN.
    • The nomogram integrates key surgical and tumor parameters, aiding perioperative decision-making and patient counseling.
    • External validation is recommended to confirm generalizability and facilitate clinical implementation.