Quantitative Prostate MRI. Issue 6 (15th May 2020)

• Record Type: Journal Article
• Authors: Schieda, Nicola; Lim, Christopher S.; Zabihollahy, Fatemeh; Abreu‐Gomez, Jorge; Krishna, Satheesh; Woo, Sungmin; Melkus, Gerd; Ukwatta, Eran; Turkbey, Baris
• Is Part Of: Journal of magnetic resonance imaging. Volume 53:Issue 6(2021)
• Page Start: 1632
• Page End: 1645
• Publication Date: 2020-05-15
• Subjects: prostate cancer -- PI‐RADS -- MRI -- machine learning -- texture analysis -- diffusion weighted imaging; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27191
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI‐RADS). PI‐RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI‐RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI‐RADS are mainly: low‐to‐moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T1 and T2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi‐ and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high‐quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. Level of Evidence: 5 Technical Efficacy: Stage 2