Multiparametric prostate MRI and structured reporting: benefits and challenges in the PI-RADS era - Mir-Bashiri, Sanas; Yaqubi, Kaneschka; Woźnicki, Piotr; Westhoff, Niklas Christian; Hardenberg, Jost von; Huber, Thomas; Froelich, Matthias F.; Sommer, Wieland H.; Reiser, Maximilian; Schönberg, Stefan; Nörenberg, Dominik (Chinese journal of academic radiology, 2021)
Prostate cancer (PCa) is the second most frequent cancer diagnosis in men and the sixth leading cause of cancer death worldwide with increasing numbers globally. Therefore, differentiated diagnostic imaging and risk-adapted therapeutic approaches are warranted. Multiparametric magnetic resonance imaging (mpMRI) of the prostate supports the diagnosis of PCa and is currently the leading imaging modality for PCa detection, characterization, local staging and image-based therapy planning. Due to the combination of different MRI sequences including functional MRI methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), mpMRI enables a high sensitivity and specificity for the detection of PCa. The rising demand for individualized treatment strategies requires methods to ensure reproducibility, completeness, and quality of prostate MRI report data. The PI-RADS (Prostate Imaging Reporting and Data System) 2.1 classification represents the classification system that is internationally recommended for MRI-based evaluation of clinically significant prostate cancer. PI-RADS facilitates clinical decision-making by providing clear reporting parameters based on clinical evidence and expert consensus. Combined with software-based solutions, structured radiology reports form the backbone to integrate results from radiomics analyses or AI-applications into radiological reports and vice versa. This review provides an overview of imaging methods for PCa detection and local staging while placing special emphasis on mpMRI of the prostate. Furthermore, the article highlights the benefits of software-based structured PCa reporting solutions implementing PI-RADS 2.1 for the integration of structured data into decision support systems, thereby paving the way for workflow automation in radiology.
Gesehen am 07.08.2023