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Predicting Positive Surgical Margins In Robot-assisted Prostatectomy Using Machine Learning Models.

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Predicting Positive Surgical Margins In Robot-assisted Prostatectomy Using Machine Learning Models.

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Predicting Positive Surgical Margins in Robot-Assisted Prostatectomy Using Machine Learning Models.

Gen Fan¹, Haochuan Chen², Yang Li¹

¹Department of Urology, School of Clinical Medicine, North Sichuan Medical College, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|May 16, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A machine learning model can predict positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP). The artificial neural network (ANN) shows promise for risk stratification, but requires further validation before clinical use.

Keywords:

artificial neural network machine learning positive surgical margin prostate cancer robotic surgery

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Retzius-Sparing Robot-Assisted Radical Prostatectomy

Retzius-Sparing Robot-Assisted Radical Prostatectomy

Published on: May 19, 2022

Area of Science:

Urology
Oncology
Artificial Intelligence

Background:

Positive surgical margins (PSM) are a concern in robot-assisted radical prostatectomy (RARP).
Accurate prediction of PSM can aid in surgical planning and patient counseling.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for predicting PSM after RARP.
Identify key predictors of PSM in this patient cohort.

Main Methods:

Retrospective analysis of 301 RARP patients.
Feature selection using the Boruta algorithm, followed by development and evaluation of seven ML models.
Optimal model interpretation using SHAP (SHapley Additive exPlanations).

Main Results:

PSM incidence was 42.0%.
The artificial neural network (ANN) model achieved the highest performance (AUC 0.808, accuracy 0.811).
Key predictors included clinical T stage and biopsy percentage; neoadjuvant therapy was protective.

Conclusions:

The developed ANN model shows potential for predicting PSM in RARP.
The model can serve as an exploratory tool for risk stratification.
External validation is necessary before clinical implementation.