Literature DB >> 32410356

Quantitative Prostate MRI.

Nicola Schieda1, Christopher S Lim2, Fatemeh Zabihollahy3, Jorge Abreu-Gomez2, Satheesh Krishna4, Sungmin Woo5, Gerd Melkus1, Eran Ukwatta6, Baris Turkbey7.   

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.
© 2020 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  MRI; PI-RADS; diffusion weighted imaging; machine learning; prostate cancer; texture analysis

Mesh:

Year:  2020        PMID: 32410356     DOI: 10.1002/jmri.27191

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  13 in total

1.  Magnetic resonance fingerprinting in prostate cancer before and after contrast enhancement.

Authors:  Young Sub Lee; Moon Hyung Choi; Young Joon Lee; Dongyeob Han; Dong-Hyun Kim
Journal:  Br J Radiol       Date:  2021-08-20       Impact factor: 3.039

Review 2.  Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.

Authors:  Durgesh Kumar Dwivedi; Naranamangalam R Jagannathan
Journal:  MAGMA       Date:  2022-07-22       Impact factor: 2.533

Review 3.  Diffusion-weighted imaging in prostate cancer.

Authors:  Tsutomu Tamada; Yu Ueda; Yoshiko Ueno; Yuichi Kojima; Ayumu Kido; Akira Yamamoto
Journal:  MAGMA       Date:  2021-09-07       Impact factor: 2.533

4.  High spectral and spatial resolution MRI of prostate cancer: a pilot study.

Authors:  Milica Medved; Aritrick Chatterjee; Ajit Devaraj; Carla Harmath; Grace Lee; Ambereen Yousuf; Tatjana Antic; Aytekin Oto; Gregory S Karczmar
Journal:  Magn Reson Med       Date:  2021-05-08       Impact factor: 4.668

Review 5.  Imaging quality and prostate MR: it is time to improve.

Authors:  Francesco Giganti; Clare Allen
Journal:  Br J Radiol       Date:  2020-11-11       Impact factor: 3.039

6.  Reproducibility of magnetic resonance fingerprinting-based T1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study.

Authors:  Nikita Sushentsev; Joshua D Kaggie; Rhys A Slough; Bruno Carmo; Tristan Barrett
Journal:  PLoS One       Date:  2021-01-29       Impact factor: 3.240

7.  The Quantitative Assessment of Using Multiparametric MRI for Prediction of Extraprostatic Extension in Patients Undergoing Radical Prostatectomy: A Systematic Review and Meta-Analysis.

Authors:  Wei Li; Yuan Sun; Yiman Wu; Feng Lu; Hongtao Xu
Journal:  Front Oncol       Date:  2021-11-22       Impact factor: 6.244

8.  DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones.

Authors:  Chie Tsuruta; Kenji Hirata; Kohsuke Kudo; Naoya Masumori; Masamitsu Hatakenaka
Journal:  Eur Radiol Exp       Date:  2022-01-12

9.  Diagnostic Performance of Extraprostatic Extension Grading System for Detection of Extraprostatic Extension in Prostate Cancer: A Diagnostic Systematic Review and Meta-Analysis.

Authors:  Wei Li; Wenwen Shang; Feng Lu; Yuan Sun; Jun Tian; Yiman Wu; Anding Dong
Journal:  Front Oncol       Date:  2022-01-25       Impact factor: 6.244

10.  MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance.

Authors:  Nikita Sushentsev; Leonardo Rundo; Oleg Blyuss; Vincent J Gnanapragasam; Evis Sala; Tristan Barrett
Journal:  Sci Rep       Date:  2021-06-21       Impact factor: 4.379

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