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Prostate cancer (PCa) is the most common malignant tumor of the male genitourinary system, the second most common cancer worldwide, and the fifth leading cause of cancer death in men. Radiomics is a noninvasive method for detecting PCa genotypes and will become a tool to assist in the diagnosis and treatment of PCa. However, one of the obstacles in transforming radiomics from research to clinical practice is its interpretability and the challenges of texture image variability. In this work, we review advances in MRI-based PCa radiomic analysis and discuss the steps and details of the radiomic flowchart. We also discuss the integration of AI with traditional medical imaging for radiomic applications, in line with the development trend of the significant data era.