  We propose an automated method for detecting aggressive prostate cancer(CaP)
(Gleason score >=7) based on a comprehensive analysis of the lesion and the
surrounding normal prostate tissue which has been simultaneously captured in
T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion
coefficient maps (ADC). The proposed methodology was tested on a dataset of 79
patients (40 aggressive, 39 non-aggressive). We evaluated the performance of a
wide range of popular quantitative imaging features on the characterization of
aggressive versus non-aggressive CaP. We found that a group of 44
discriminative predictors among 1464 quantitative imaging features can be used
to produce an area under the ROC curve of 0.73.
