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Machine Learning to Predict Prostate Artery Embolization Outcomes.

G Vigneswaran^1,2, N Doshi^1,2, D Maclean¹

¹Department of Interventional Radiology, University Hospital Southampton, Southampton, UK.

Cardiovascular and Interventional Radiology

|June 19, 2024

Summary

This summary is machine-generated.

Machine learning models predict one-year outcomes for prostate artery embolization (PAE) patients. The tool uses pre-procedural data to forecast International Prostate Symptom Score (IPSS) changes, aiding in patient counseling before treatment.

Keywords:

Artificial intelligence Embolization Prostate

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Area of Science:

Urology
Medical Informatics
Machine Learning

Background:

Prostate artery embolization (PAE) is a treatment for benign prostatic hyperplasia.
Predicting patient outcomes after PAE is crucial for treatment planning and patient counseling.
Current methods for outcome prediction may not fully leverage pre-procedural data.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting one-year outcomes after PAE.
To utilize pre-procedural patient data to forecast changes in International Prostate Symptom Score (IPSS).
To create an interactive tool for real-time prediction to assist in patient consultations.

Main Methods:

Retrospective analysis of data from the UK-ROPE registry and a single institution (2012-2023).
Application of various ML models (linear regression, lasso, ridge, decision trees, random forests) with cross-validation.
Prediction of baseline IPSS and 1-year change using clinical and urodynamic parameters.

Main Results:

ML models showed reasonable performance in predicting baseline IPSS and its change at one year.
Mean absolute error for baseline IPSS prediction ranged from 4.9-7.3.
Model error in predicting baseline IPSS correlated significantly with 1-year IPSS change (R²=0.2, p<0.001).

Conclusions:

ML models can effectively predict one-year IPSS improvement following PAE.
An integrated, user-friendly digital interface allows for real-time outcome prediction.
This predictive tool can enhance pre-treatment patient counseling and inform clinical decision-making.