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Updated: Jul 31, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

A treatment prediction strategy for overactive bladder using a machine learning algorithm that utilized data from the

Farid Hadi¹, Budiwan Sumarsono¹, Kyu-Sung Lee²

¹Astellas Pharma Medical Affairs, Singapore, Singapore.

Neurourology and Urodynamics

|May 6, 2023

Summary

Machine learning accurately predicts overactive bladder treatment response to mirabegron or antimuscarinics. The developed decision tree model offers a valuable clinical decision-making aid.

Keywords:

incontinence machine learning mathematical or statistical modeling overactive bladder questionnaire urgency/frequency

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Area of Science:

Urology
Pharmacology
Data Science

Background:

Overactive bladder (OAB) affects millions globally, necessitating effective treatment strategies.
Current treatments include mirabegron and antimuscarinic agents, but predicting individual patient response remains challenging.

Purpose of the Study:

To develop and validate a machine learning model for predicting treatment response in OAB patients.
To utilize real-world data from the FAITH registry for model development.

Main Methods:

Data from 396 OAB patients initiating mirabegron or antimuscarinic monotherapy were analyzed.
A composite outcome defined treatment effectiveness based on efficacy, persistence, and safety.
A decision tree (C5.0) model was optimized using 14 clinical risk factors and 10-fold cross-validation.

Main Results:

The final optimized decision tree model achieved an area under the curve of 0.70.
The model demonstrated predictive capability for treatment response in OAB patients.
Patient groups receiving mirabegron or antimuscarinics showed comparable baseline characteristics.

Conclusions:

A simple, rapid, and user-friendly machine learning interface was successfully developed.
The model has the potential to serve as an educational tool or clinical decision-making aid for OAB treatment selection.