Prostate cancer is a disease with very high prevalence and mortality in the western world. An early accurate diagnosis can increase treatment efficiency. Current diagnosing techniques consist in systematic biopsy sampling. Radiomics can infer tumor's phenotypic differentiations from medical images, providing an accurate guide for biopsy sampling and making personalized treatment plans possible. Radiomics are various features that are extracted from medical images. Subsequently they are applied to train machine learning models that distinguish between healthy or cancerous tissue. In this thesis a software routine that extracts the most commonly reported MRI prostate cancer radiomic features was built. Then, several classification methods were tried. Results were validated on T2 MRI patient images with confirmed histopathology from two different clinics.