The aim of this study is to provide a noninvasive, radiological image-based Computer Aided Diagnosis (CAD) able to distinguish between high aggressive (Gleason Score (GS) >= 4+3) and low-aggressive (GS<= 3+4) Prostate Cancers (PCa). The system exploits the use of Machine Learning (ML) and Deep Learning (DL) on biparametric Magnetic Resonance Images coming from Candiolo IRCCS and San Giovanni Molinette hospital. Regarding the ML approach, once tumor areas have been manually segmented, features of first order statistics, intensity-based and texture features are extracted, both from T2WI and ADC maps. The study carries out a parallel analysis of ten different Datasets, which differ in type of feature (3D or 2D), voxel spacing, application of filters, and bin number.