Multiparametric magnetic resonance imaging of the prostate: Lights and shadows. Issue 4 (November 2021)

• Record Type: Journal Article
• Authors: Santoro, Agostino Antonio; Di Gianfrancesco, Luca; Racioppi, Marco; Pinto, Francesco; Palermo, Giuseppe; Sacco, Emilio; Campetella, Marco; Scarciglia, Eros; Bientinesi, Riccardo; Di Paola, Valerio; Totaro, Angelo
• Is Part Of: Urologia journal. Volume 88:Issue 4(2021)
• Page Start: 280
• Page End: 286
• Publication Date: 2021-11
• Subjects: Multiparametric prostate magnetic resonance imaging -- clinically significant prostate cancer -- active surveillance -- fusion targeted biopsy -- prostate cancer staging -- PI-RADS score -- prostate cancer; Urology -- Periodicals; 616.6005
• DOI: 10.1177/03915603211019982
• Languages: English
• ISSNs: 0391-5603

Abstract

Prostate cancer is the second most commonly diagnosed cancer in man. Since the first MRI was performed, enormous progress has been made in diagnosis, treatment, and follow up of PCa, mainly due to multiparametric prostatic MRI (mpMRI). Although mpMRI has become the best imaging tool for identifying PCa, some limitations still exist. Prostate imaging with mpMRI is, to date, the best way to locate suspicious lesions to trigger prostate biopsy, plan active surveillance, or definitive treatment. In case of relapse, mpMRI can help detect local disease and provide specific management. It is well known that there is a subset of patients in whom mpMRI fails to depict csPCa. These missed significant cancers demand great attention. Prostate mpMRI quality depends on several factors related to equipment (including equipment vendor, magnet field and gradient strength, coil set used, software and hardware levels, sequence parameter choices), patient (medications, body habitus, motion, metal implants, rectal gas), and most importantly the radiologic interpretation of images (learning curve effects, subjectivity of observations, interobserver variations, and reporting styles). Inter-reader variability represents a huge current limitation of this method. Therefore, mpMRI remains the best imaging tool available to detect PCa, guiding diagnosis, treatment, and follow up while inter-reader variability represents the best limitation. Radiomics can help identifying imaging biomarkers to help radiologist in detecting significant PCa, reducing examination times, and costs.