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

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Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a

Yu Sun^1,2, Hayley Reynolds^3,4, Darren Wraith⁵

¹The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia. yu.sun@petermac.org.

Australasian Physical & Engineering Sciences in Medicine

|January 26, 2017

Summary

A support vector machine (SVM) algorithm accurately predicted prostate tumor location using multi-parametric MRI (mpMRI). This aids in planning bio-focused radiotherapy by identifying tumor sites with high precision.

Keywords:

Bio-focused therapy Focal therapy Machine learning Multiparametric MRI Prostate cancer Support vector machines

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Area of Science:

Medical Imaging
Machine Learning
Oncology

Background:

Accurate prostate tumor localization is crucial for effective radiotherapy.
Multi-parametric MRI (mpMRI) offers detailed imaging for prostate cancer assessment.

Purpose of the Study:

To evaluate a support vector machine (SVM) algorithm for predicting prostate tumor location using mpMRI data.
To inform the implementation of bio-focused radiotherapy through precise tumor site identification.

Main Methods:

In vivo mpMRI data (T2-weighted, diffusion-weighted, dynamic contrast-enhanced) from 16 patients were collected.
mpMRI data were registered with histology (ground truth) using ex vivo MRI.
A Gaussian kernel SVM was trained and tested, using signal intensities as features and histology annotations as labels.

Main Results:

The SVM achieved prediction accuracies ranging from 70.4% to 87.1%.
Area under the curve (AUC) of the receiver operating characteristics (ROC) curve ranged from 0.81 to 0.94.
Apparent diffusion coefficient maps from diffusion-weighted imaging were the most critical modality for prediction.

Conclusions:

The SVM algorithm demonstrates significant potential for accurate prostate tumor localization using mpMRI.
This approach can enhance the precision of bio-focused radiotherapy planning.
Future work will expand the dataset and incorporate tumor biological characteristic predictions.