Magnetic resonance imaging radiomics in prostate cancer radiology: what is currently known?

Abstract

Diagnostic and treatment approaches in prostate cancer rely on a combination of magnetic resonance imaging and histological data. This study aimed to introduce the basics of the current diagnostic approach in prostate cancer with a focus on texture analysis. Texture analysis evaluates the relationships between image pixels using mathematical methods, which provide additional information. First-order texture analysis of features can have greater clinical reproducibility than higher-order texture features. Textural features that are extracted from diffusion coefficient maps have shown the greatest clinical relevance. Future research should focus on integrating machine learning methods to facilitate the use of texture analysis in clinical practice. The development of automated segmentation methods is required to reduce the likelihood of including normal tissue in the area of interest. Texture analysis allows the noninvasive separation of patients into groups in terms of possible treatment options. Currently, few clinical studies reported on the differential diagnosis of clinically significant prostate cancer, including the Gleason and International Society of Urological Pathology grading. Large prospective studies are required to verify the diagnostic potential of textural features.