[Advances in artificial intelligence-assisted MRI radiomics in the diagnosis and treatment of prostate cancer]

Prostate cancer (PCa) is the second most common cancer worldwide and the fifth leading cause of cancer deaths in men. Magnetic resonance imaging (MRI), with its high sensitivity and specificity in detecting PCa, is currently the most widely used imaging technique for tumor localization and staging. MRI plays a significant role in risk stratification of patients with neoplasm, surveillance of low-risk patients, and monitoring of recurrence after treatment. Radiomics is an emerging and promising tool that allows quantitative assessment of tumors in images by converting digital images into mineable high-dimensional data. Imaging histology aims to increase the number of features that can be used to detect PCa, avoid unnecessary biopsies, determine tumor aggressiveness and monitor recurrence after treatment. Artificial intelligence integration of imaging histology data, including those of different imaging modalities (e.g., PET-CT) as well as other clinical and histopathological data, can improve the prediction of tumor aggressiveness and guide clinical decision-making and patient management. The aim of this review is to present current research applications of AI-assisted radiomics in PCa MRI images.