Literature DB >> 28546032

Application of Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2): Interobserver Agreement and Positive Predictive Value for Localization of Intermediate- and High-Grade Prostate Cancers on Multiparametric Magnetic Resonance Imaging.

Frank Chen1, Steven Cen2, Suzanne Palmer2.   

Abstract

RATIONALE AND
OBJECTIVES: To evaluate interobserver agreement with the use of and the positive predictive value (PPV) of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) for the localization of intermediate- and high-grade prostate cancers on multiparametric magnetic resonance imaging (mpMRI).
MATERIALS AND METHODS: In this retrospective, institutional review board-approved study, 131 consecutive patients who had mpMRI followed by transrectal ultrasound-MR imaging fusion-guided biopsy of the prostate were included. Two readers who were blinded to initial mpMRI reports, clinical data, and pathologic outcomes reviewed the MR images, identified all prostate lesions, and scored each lesion based on the PI-RADS v2. Interobserver agreement was assessed by intraclass correlation coefficient (ICC), and PPV was calculated for each PI-RADS category.
RESULTS: PI-RADS v2 was found to have a moderate level of interobserver agreement between two readers of varying experience, with ICC of 0.74, 0.72, and 0.67 for all lesions, peripheral zone lesions, and transitional zone lesions, respectively. Despite only moderate interobserver agreement, the calculated PPV in the detection of intermediate- and high-grade prostate cancers for each PI-RADS category was very similar between the two readers, with approximate PPV of 0%, 12%, 64%, and 87% for PI-RADS categories 2, 3, 4, and 5, respectively.
CONCLUSIONS: In our study, PI-RADS v2 has only moderate interobserver agreement, a similar finding in studies of the original PI-RADS and in initial studies of PI-RADS v2. Despite this, PI-RADS v2 appears to be a useful system to predict significant prostate cancer, with PI-RADS scores correlating well with the likelihood of intermediate- and high-grade cancers.
Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  PI-RADS; Prostate cancer; TRUS-MR imaging fusion biopsy; interobserver agreement; multiparametric magnetic resonance imaging

Mesh:

Year:  2017        PMID: 28546032     DOI: 10.1016/j.acra.2017.03.019

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


  8 in total

1.  Multiparametric MR imaging of peripheral zone prostate cancer: effect of postbiopsy hemorrhage on cancer detection according to Gleason score and tumour volume.

Authors:  Sung Il Jung; Hae Jeong Jeon; Hee Sun Park; Mi Hye Yu; Young Jun Kim; Seung Eun Lee; So Dug Lim
Journal:  Br J Radiol       Date:  2018-03-09       Impact factor: 3.039

2.  Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI.

Authors:  Matthew D Greer; Joanna H Shih; Nathan Lay; Tristan Barrett; Leonardo Bittencourt; Samuel Borofsky; Ismail Kabakus; Yan Mee Law; Jamie Marko; Haytham Shebel; Maria J Merino; Bradford J Wood; Peter A Pinto; Ronald M Summers; Peter L Choyke; Baris Turkbey
Journal:  AJR Am J Roentgenol       Date:  2019-03-27       Impact factor: 3.959

3.  Serial prostate magnetic resonance imaging fails to predict pathological progression in patients on active surveillance.

Authors:  Danly Omil-Lima; Albert Kim; Ilon Weinstein; Karishma Gupta; David Sheyn; Lee Ponsky
Journal:  Can Urol Assoc J       Date:  2022-07       Impact factor: 2.052

4.  Prostate Imaging-Reporting and Data System Steering Committee: PI-RADS v2 Status Update and Future Directions.

Authors:  Anwar R Padhani; Jeffrey Weinreb; Andrew B Rosenkrantz; Geert Villeirs; Baris Turkbey; Jelle Barentsz
Journal:  Eur Urol       Date:  2018-06-13       Impact factor: 20.096

5.  Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes.

Authors:  Jorge Abreu-Gomez; Daniel Walker; Tareq Alotaibi; Matthew D F McInnes; Trevor A Flood; Nicola Schieda
Journal:  Eur Radiol       Date:  2020-03-24       Impact factor: 5.315

6.  Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Authors:  Bino Varghese; Frank Chen; Darryl Hwang; Suzanne L Palmer; Andre Luis De Castro Abreu; Osamu Ukimura; Monish Aron; Manju Aron; Inderbir Gill; Vinay Duddalwar; Gaurav Pandey
Journal:  Sci Rep       Date:  2019-02-07       Impact factor: 4.379

7.  Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer.

Authors:  Mou Li; Ling Yang; Yufeng Yue; Jingxu Xu; Chencui Huang; Bin Song
Journal:  Front Oncol       Date:  2021-02-17       Impact factor: 6.244

8.  Semi-automated PIRADS scoring via mpMRI analysis.

Authors:  Nikhil J Dhinagar; William Speier; Karthik V Sarma; Alex Raman; Adam Kinnaird; Steven S Raman; Leonard S Marks; Corey W Arnold
Journal:  J Med Imaging (Bellingham)       Date:  2020-12-29
  8 in total

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