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\éectiveness and robustness of HS biomarkers for prostate cancer diagnosis have not been explored. In this study, biomarkers from di\éerent data distributions are constructed. Results show that HS biomarkers can achieve better performances in di\éerent data distributions.
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