Prostate specific antigen (PSA) screening is routinely conducted for suspected prostate cancer (PCa) patients. As this technique might result in high probability of over-diagnosis and unnecessary prostate biopsies, controversies on it remains especially for patients with “gray-zone” PSA levels, i.e. 4-10ng/ml. To improve the risk stratification of suspected PCa patients, Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) was released in 2015. Although PI-RADSv2 showed good performance in the detection of PCa, its specificity was relatively low for patients with gray-zone PSA levels. This indicated that over-diagnosis issue could not be dealt well by PI-RADSv2 in the gray zone. Addressing this, we attempted to validate whether radiomics analysis of Diffusion weighted Imaging (DWI) data could reduce over-diagnosis of PCa with gray-zone PSA levels. Here, 140 suspected PCa patients in Peking Union Medical College Hospital were enrolled. 700 radiomic features were extracted from the DWI data. Least absolute shrinkage and selection operator (LASSO) were conducted, and 7 radiomic features were selected on the training set (n=93). Based on these features, random forest classifier was used to build the Radiomics model, which performed better than PI-RADSv2 (area under the curve [AUC]: 0.900 vs 0.773 and 0.844 vs 0.690 on the training and test sets). Furthermore, the specificity values of Radiomics model and PI-RADSv2 was 0.815 and 0.481 on the test set, respectively. In conclusion, radiomics analysis of DWI data might reduce the over-diagnosis of PCa with gray-zone PSA levels.