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

Content Provider	Springer Nature Link
Author	Sun, Yu Reynolds, Hayley Wraith, Darren Williams, Scott Finnegan, Mary E. Mitchell, Catherine Murphy, Declan Ebert, Martin A. Haworth, Annette
Copyright Year	2017
Abstract	The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with ‘ground truth’ histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.
Starting Page	39
Ending Page	49
Page Count	11
File Format	PDF
ISSN	01589938
Journal	Australasian Physics & Engineering Sciences in Medicine
Volume Number	40
Issue Number	1
e-ISSN	18795447
Language	English
Publisher	Springer Netherlands
Publisher Date	2017-01-24
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Prostate cancer Multiparametric MRI Machine learning Support vector machines Focal therapy Bio-focused therapy Biomedicine Biological and Medical Physics, Biophysics Medical and Radiation Physics Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Physics and Astronomy Biophysics Biomedical Engineering

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