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Multiparametric MRI - local staging of prostate cancer and beyond

Open Access
|May 2019

Abstract

Background

Accurate local staging is critical for treatment planning and prognosis in patients with prostate cancer (PCa). The primary aim is to differentiate between organ-confined and locally advanced disease with the latter carrying a worse clinical prognosis. Multiparametric MRI (mpMRI) is the imaging modality of choice for the local staging of PCa and has an incremental value in assessing pelvic nodal disease and bone involvement. It has shown superior performance compared to traditional staging based on clinical nomograms, and provides additional information on the site and extent of disease. MRI has a high specificity for diagnosing extracapsular extension (ECE), seminal vesicle invasion (SVI) and lymph node (LN) metastases, however, sensitivity remains poor. As a result, extended pelvic LN dissection remains the gold standard for assessing pelvic nodal involvement, and there has been recent progress in developing advanced imaging techniques for more distal staging.

Conclusions

T2W-weighted imaging is the cornerstone for local staging of PCa. Imaging at 3T and incorporating both diffusion weighted and dynamic contrast enhanced imaging can further increase accuracy. “Next generation” imaging including whole body MRI and PET-MRI imaging using prostate specific membrane antigen (68Ga-PSMA), has shown promising for assessment of LN and bone involvement as compared to the traditional work-up using bone scintigraphy and body CT.

DOI: https://doi.org/10.2478/raon-2019-0021 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Language: English
Page range: 159 - 170
Submitted on: Mar 29, 2019
Accepted on: Apr 15, 2019
Published on: May 8, 2019
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Iztok Caglic, Viljem Kovac, Tristan Barrett, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.