Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. An automated or semi-automated PI-RADS assessment system could be beneficial in the screening process of PCa and could improve the consistency of scoring. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automatic framework for PI-RADS assessment using machine learning classifiers.
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