Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

David Jean Winkel, Hanns-Christian Breit, Bibo Shi, Daniel T. Boll, Hans-Helge Seifert, Christian Wetterauer