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Prostate cancer (PCa) is the second most common cancer type in men, and the need for a non-invasive PCa diagnostic and stratification tool can now be met through the quantified analysis of multiparametric MRI. Early diagnosis of clinically significant prostate cancer affects patient management. Radiomics analysis can quantify imaging information in a region of interest (ROI) that is not visible to clinicians, thereby improving cancer detection. Radiomic based models have been proposed for the diagnosis of csPCa, mainly focusing on the evaluation of lesions, a process that is both challenging and time consuming. On the other hand, automation of whole gland segmentation is achieved using deep learning algorithms and radiomic-based model considering whole prostate gland have shown promising results. In this study we investigate whether radiomics from different parts of the gland affect classification performance. T2 weighted and diffusion weighted images (DWI), of different b values, from 80 patients were analyzed. Radiomics were extracted from the gland, the transition and periphery zone and areas with predefines widths. Recursive feature elimination together with a voting strategy was used for feature selection and two machine learning models were trained and tested. The results revealed that the best diagnostic accuracy of 81.9% (±7.8) was succeeded using the radiomics from the transition and periphery zone whereas, DWI of high b value (800) is the most informative imaging modality. These findings highlight the need for the development of an accurate segmentation algorithm which can improve the development of accurate and robust models for the diagnosis of csPCa.Clinical Relevance- This work can guide radiomic research for the development of accurate and robust models for the diagnosis of csPCa assisting clinicians in patient evaluation and management.