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Abstract #2560

Artificial Neural Networks in Radiological Predictive Models

Nikolaos Dikaios1, Taiki Fujiwara2, David Atkinson3, Shonit Punwani2

1Department of Medical Physics and Bioengineering, University College London, London, Greater London, United Kingdom; 2Department of Radiology, University College London Hospital; 3University College London, Centre for Medical Imaging

Predictive models are being increasingly employed in radiology as diagnostic aids for cancer detection. A variety of model types exist. Linear discriminant analysis (LDA) models assume linearity, normality and that the input variables are independent, assumptions which may affect classification accuracy. Neural networks (NN) whilst less intuitive, do not make these assumptions and can detect complex non-linear relationships between the input variables. Both LDA and NN are prone to over-fitting. In this work we compared the performance of multilayer perceptron (MLP) artificial NN and LDA models for prediction of transition zone (TZ) prostate cancer (based on quantitative multi-parametric MRI variables) using a leave-one-out (LOO) and a 2-fold cross validation analysis.