Prostate specific antigen (PSA)-based screening reduces the rate of death from prostate cancer (PCa) by 31%, but this
benefit is associated with a high risk of overdiagnosis and overtreatment. As prostate transrectal ultrasound-guided
biopsy, the standard procedure for prostate histological sampling, has a sensitivity of 77% with a considerable false-negative rate, more accurate methods need to be found to detect or rule out significant disease. Prostate magnetic
resonance imaging has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive
detection tool, in particular exploiting the combination of anatomical and functional information in a multiparametric
framework. The purpose of this study was to describe a computer aided diagnosis (CAD) method that automatically
produces a malignancy likelihood map by combining information from dynamic contrast enhanced MR images and
diffusion weighted images. The CAD system consists of multiple sequential stages, from a preliminary registration of images of different sequences, in order to correct for susceptibility deformation and/or movement artifacts, to a Bayesian classifier, which fused all the extracted features into a probability map. The promising results (AUROC=0.87) should be validated on a larger dataset, but they suggest that the discrimination on a voxel basis between benign and malignant tissues is feasible with good performances. This method can be of benefit to improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the reading time, automatically highlighting probably cancer suspicious regions.