Abstract #1047
Support Vector Neural Networks versus Logistic Regression MR based diagnostic model for classification of transition zone prostate cancer
Nikolaos Dikaios 1,2 , Jokha Alkalbani 2 , Alex Kirkham 3 , Clare Allen 3 , Hashim Ahmed 4 , Mark Emberton 4 , Alex Freeman 5 , Steve Halligan 2 , Stuart Taylor 2 , David Atkinson 2 , and Shonit Punwani 2
1
Medical Physics, UCL, London, Greater
London, United Kingdom,
2
Centre
of Medical Imaging, UCL, Greater London, United Kingdom,
3
Radiology,
UCL, Greater London, United Kingdom,
4
Urology,
UCL, Greater London, United Kingdom,
5
Histopathology,
UCL, Greater London, United Kingdom
Multi-parametric MRI (mp-MRI) facilitates identification
of transition zone cancers, yet its overall diagnostic
accuracy is likely lower in this part of the prostate
compared with the peripheral zone. Benign hyperplastic
nodules within the transition zone likely make the
localisation of cancer difficult. Logistic regression
(LR) models1 for classifying transition zone (TZ)
prostate cancer (PCa) on mp-MRI were previously derived
and validated. Here we explore whether the application
of support vector machine (SVM) neural network (SVNN)
algorithms can improve classification accuracy. The
proposed SVNN algorithm is trained on 70 patients and
temporally validated on a second independent cohort of
85 patients.
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