  Prostate Imaging Reporting and Data System (PI-RADS) based on
multi-parametric MRI classi\^ees patients into 5 categories (PI-RADS 1-5) for
routine clinical diagnosis guidance. However, there is no consensus on whether
PI-RADS 3 patients should go through biopsies. Mining features from these hard
samples (HS) is meaningful for physicians to achieve accurate diagnoses.
Currently, the mining of HS biomarkers is insu\`icient, and the e\'eectiveness
and robustness of HS biomarkers for prostate cancer diagnosis have not been
explored. In this study, biomarkers from di\'eerent data distributions are
constructed. Results show that HS biomarkers can achieve better performances in
di\'eerent data distributions.
