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Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical

Mithat Ekşi1, İsmail Evren1, Fatih Akkaş1

  • 1Department of Urology, Istanbul Bakirkoy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.

The Prostate
|July 5, 2021
PubMed
Summary

Machine learning models accurately predict biochemical recurrence (BCR) after prostatectomy, outperforming traditional methods. These advanced tools offer better risk classification and prognosis for patients, improving clinical decision-making.

Keywords:
artificial intelligencebiochemical recurrencemachine learningprostate cancerprostatectomyrobot assisted

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Area of Science:

  • Urology
  • Oncology
  • Data Science

Background:

  • Biochemical recurrence (BCR) after prostatectomy impacts patient prognosis.
  • Accurate prediction of BCR is crucial for long-term patient management.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting BCR in patients with long follow-up periods.
  • To compare the performance of ML models against conventional statistical methods.

Main Methods:

  • Retrospective review of 368 patients undergoing robot-assisted radical prostatectomy.
  • Application of Random Forest, K-Nearest Neighbour, and Logistic Regression ML algorithms.
  • Analysis of clinical parameters including demographics, preoperative/postoperative data, and pathological findings.

Main Results:

  • ML models demonstrated superior performance in predicting BCR compared to Cox regression.
  • Random Forest achieved an Area Under the Curve (AUC) of 0.95.
  • Predictive factors for BCR included NLR, PSAd, risk classification, PIRADS score, T stage, surgical margin status, and seminal vesicle invasion.

Conclusions:

  • ML models offer a more reliable and potent approach for BCR prediction.
  • These models can enhance risk stratification, prognosis estimation, and guide treatment decisions.
  • The clinical utility of ML in prostate cancer management is expected to grow with increased data.