Timely diagnosis and treatment could effectively reduce patient risk for clinical significant prostate cancer (PCa). In this abstract, we extracted 327 quantitative features from prostate mp-MRI images, then we used a homemade open-source tool named Feature Explorer to study combinations of radiomics algorithms and hyper-parameters in order to find the best model for classification of PCa into non-clinical–significant and clinical significant. We obtained a candidate model with AUC of 0.823, accuracy of 0.827. Four features selected for classification are easily understandable in the sense of image characteristics. Feature Explorer was demonstrated to be an efficient tool for radiomics studies.