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

  • Yu SunEmail author
  • Hayley Reynolds
  • Darren Wraith
  • Scott Williams
  • Mary E. Finnegan
  • Catherine Mitchell
  • Declan Murphy
  • Martin A. Ebert
  • Annette Haworth
Scientific Paper

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.

Keywords

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

Notes

Acknowledgements

This research is supported by the Prostate Cancer Foundation of Australia, The University of Melbourne and Cancer Therapeutics CRC. The authors would also like to show their gratitude to Courtney Savill and Lauren Caspersz who have made substantial contributions during data collections.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical Approval

Ethics in this article is approved by Human Research Ethics Committee (HREC).

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2017

Authors and Affiliations

  1. 1.The Sir Peter MacCallum Department of OncologyThe University of MelbourneMelbourneAustralia
  2. 2.Department of Physical SciencesPeter MacCallum Cancer CentreMelbourneAustralia
  3. 3.Institute of Health and Biomedical InnovationQueensland University of TechnologyBrisbaneAustralia
  4. 4.Division of Radiation Oncology and Cancer ImagingPeter MacCallum Cancer CentreMelbourneAustralia
  5. 5.Department of ImagingImperial College Healthcare NHS TrustLondonUK
  6. 6.Department of BioengineeringImperial College LondonLondonUK
  7. 7.Department of PathologyPeter MacCallum Cancer CentreMelbourneAustralia
  8. 8.Division of Cancer SurgeryPeter MacCallum Cancer CentreMelbourneAustralia
  9. 9.Department of Radiation OncologySir Charles Gairdner HospitalPerthAustralia
  10. 10.School of PhysicsUniversity of Western AustraliaPerthAustralia

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