Literature DB >> 32143993

Quality of Prostate MRI: Is the PI-RADS Standard Sufficient?

Jonathan Sackett1, Joanna H Shih2, Sarah E Reese3, Jeffrey R Brender4, Stephanie A Harmon5, Tristan Barrett6, Mehmet Coskun7, Manuel Madariaga8, Jamie Marko9, Yan Mee Law10, Evrim B Turkbey9, Sherif Mehralivand11, Thomas Sanford11, Nathan Lay11, Peter A Pinto12, Bradford J Wood13, Peter L Choyke11, Baris Turkbey14.   

Abstract

RATIONALE AND
OBJECTIVE: The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) published a set of minimum technical standards (MTS) to improve image quality and reduce variability in multiparametric prostate MRI. The effect of PIRADSv2 MTS on image quality has not been validated. We aimed to determine whether adherence to PI-RADSv2 MTS improves study adequacy and perceived quality.
MATERIALS AND METHODS: Sixty-two prostate MRI examinations including T2 weighted (T2W) and diffusion weighted image (DWI) consecutively referred to our center from 62 different institutions within a 12-month period (September 2017 to September 2018) were included. Six readers assessed images as adequate or inadequate for use in PCa detection and a numerical image quality ranking was given using a 1-5 scale. The PI-RADSv2 MTS were synthesized into sets of seven and 10 rules for T2W and DWI, respectively. Image adherence was assessed using Digital Imaging and Communications in Medicine (DICOM) metadata. Statistical analysis of survey results and image adherence was performed based on reader quality scoring (Kendall Rank tau-b) and reader adequate scoring (Wilcoxon test for association) for T2 and DWI quality assessment.
RESULTS: Out of 62 images, 52 (83%) T2W and 38 (61%) DWIs were rated to be adequate by a majority of readers. Reader adequacy scores showed no significant association with adherence to PI-RADSv2. There was a weak (tau-b = 0.22) but significant (p value = 0.01) correlation between adherence to PIRADSv2 MTS and image quality for T2W. Studies following all PI-RADSv2 T2W rules achieved a higher median average quality score (3.58 for 7/7 vs. 3.0 for <7/7, p = 0.012). No statistical relationship with PI-RADSv2 MTS adherence and DWI quality was found.
CONCLUSION: Among 62 sites performing prostate MRI, few were considered of high quality, but the majority were considered adequate. DWI showed considerably lower rates of adequate studies in the sample. Adherence to PI-RADSv2 MTS did not increase the likelihood of having a qualitatively adequate T2W or DWI.
Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diffusion weighted imaging; MRI; PI-RADS; Prostate; Quality control

Mesh:

Year:  2020        PMID: 32143993     DOI: 10.1016/j.acra.2020.01.031

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

Review 1.  Deep learning-based artificial intelligence applications in prostate MRI: brief summary.

Authors:  Baris Turkbey; Masoom A Haider
Journal:  Br J Radiol       Date:  2021-12-03       Impact factor: 3.039

Review 2.  Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway.

Authors:  Tristan Barrett; Maarten de Rooij; Francesco Giganti; Clare Allen; Jelle O Barentsz; Anwar R Padhani
Journal:  Nat Rev Urol       Date:  2022-09-27       Impact factor: 16.430

Review 3.  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

4.  Better Image Quality for Diffusion-weighted MRI of the Prostate Using Deep Learning.

Authors:  Baris Turkbey
Journal:  Radiology       Date:  2022-02-01       Impact factor: 11.105

Review 5.  Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score.

Authors:  Francesco Giganti; Veeru Kasivisvanathan; Alex Kirkham; Shonit Punwani; Mark Emberton; Caroline M Moore; Clare Allen
Journal:  Br J Radiol       Date:  2021-07-08       Impact factor: 3.039

6.  Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality.

Authors:  Stefano Cipollari; Valerio Guarrasi; Martina Pecoraro; Marco Bicchetti; Emanuele Messina; Lorenzo Farina; Paola Paci; Carlo Catalano; Valeria Panebianco
Journal:  J Magn Reson Imaging       Date:  2021-08-09       Impact factor: 5.119

Review 7.  Optimal biopsy approach for detection of clinically significant prostate cancer.

Authors:  Simona Ippoliti; Peter Fletcher; Luca Orecchia; Roberto Miano; Christof Kastner; Tristan Barrett
Journal:  Br J Radiol       Date:  2021-08-06       Impact factor: 3.039

  7 in total

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