Literature DB >> 27115311

Validation of PI-RADS v.2 for prostate cancer diagnosis with MRI at 3T using an external phased-array coil.

Matteo Baldisserotto1, Eurico J Dornelles Neto2, Gustavo Carvalhal2, Aloyso F de Toledo3, Clovis M de Almeida3, Carlos E D Cairoli3, Daniel O de Silva3, Eduardo Carvalhal3, Ricardo P Paganin4, Alexandre Agra3, Francisco S de Santos5, Jorge A P Noronha2.   

Abstract

PURPOSE: To date, few studies have validated the Prostate Imaging Reporting and Data System Version 2 (PI-RADS v. 2) for the diagnosis of prostate cancer. Our aim was to validate PI-RADS v.2 using 3 Tesla (T) MRI.
MATERIALS AND METHODS: This is a retrospective study of 54 consecutive patients who underwent 3T MRI with a body-array coil for diagnostic confirmation of prostate cancer or cancer staging between June 2013 and June 2015. Sensitivity, specificity, and agreement were calculated based on a criterion of PI-RADS score = 3. Inter-examiner agreement was determined by the weighted kappa statistic.
RESULTS: Histological findings were positive for cancer in 33 patients and negative in 21 patients. Considering a PI-RADS score of 3 as positive for cancer, the accuracy of each reader was 85.20% and 70.40%, respectively, and agreement coefficients were κ = 0.69 and κ = 0.35. Considering PI-RADS 3 as absence of cancer, the accuracy of each reader was 77.80% and 77.80%, respectively, and agreement was κ = 0.55 and κ = 0.54. Inter-reader agreement was moderate/good (weighted κ = 0.53; 95% confidence interval: 0.39-0.66; P = 0.038).
CONCLUSION: High accuracy was obtained for the diagnosis of prostate cancer using 3T MRI with a body coil and the PI-RADS v.2 score. J. Magn. Reson. Imaging 2016;44:1354-1359.
© 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  magnetic resonance imaging; prostate cancer; scoring method; validation studies

Mesh:

Year:  2016        PMID: 27115311     DOI: 10.1002/jmri.25284

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


  16 in total

Review 1.  A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer.

Authors:  Li Zhang; Min Tang; Sipan Chen; Xiaoyan Lei; Xiaoling Zhang; Yi Huan
Journal:  Eur Radiol       Date:  2017-06-27       Impact factor: 5.315

2.  Validation of Prostate Imaging-Reporting and Data System Version 2: A Retrospective Analysis.

Authors:  Michael Nguyentat; Alexander Ushinsky; Alessandra Miranda-Aguirre; Edward Uchio; Chandana Lall; Layla Shirkhoda; Thomas Lee; Christopher Green; Roozbeh Houshyar
Journal:  Curr Probl Diagn Radiol       Date:  2017-10-12

Review 3.  PI-RADS v2: Current standing and future outlook.

Authors:  Clayton P Smith; Barış Türkbey
Journal:  Turk J Urol       Date:  2018-05-01

4.  Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

Authors:  Jing Wang; Chen-Jiang Wu; Mei-Ling Bao; Jing Zhang; Xiao-Ning Wang; Yu-Dong Zhang
Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

5.  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

6.  Development and internal validation of PI-RADs v2-based model for clinically significant prostate cancer.

Authors:  Yu Zhang; Na Zeng; Yi Chen Zhu; Yang Xin Rui Huang; Qiang Guo; Ye Tian
Journal:  World J Surg Oncol       Date:  2018-06-01       Impact factor: 2.754

7.  Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment.

Authors:  Chang Liu; Shi-Liang Liu; Zhi-Xian Wang; Kai Yu; Chun-Xiang Feng; Zan Ke; Liang Wang; Xiao-Yong Zeng
Journal:  Asian J Androl       Date:  2018 Sep-Oct       Impact factor: 3.285

Review 8.  Prostate Cancer Biomarker Development: National Cancer Institute's Early Detection Research Network Prostate Cancer Collaborative Group Review.

Authors:  Michael A Liss; Robin J Leach; Martin G Sanda; Oliver J Semmes
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-10-22       Impact factor: 4.254

9.  Optimizing MRI-targeted prostate biopsy: the diagnostic benefit of additional targeted biopsy cores.

Authors:  Chad R Tracy; Kevin J Flynn; Daniel D Sjoberg; Paul T Gellhaus; Catherine M Metz; Behfar Ehdaie
Journal:  Urol Oncol       Date:  2020-10-27       Impact factor: 2.954

10.  Diagnostic Performance and Interobserver Consistency of the Prostate Imaging Reporting and Data System Version 2: A Study on Six Prostate Radiologists with Different Experiences from Half a Year to 17 Years.

Authors:  Zan Ke; Liang Wang; Xiang-De Min; Zhao-Yan Feng; Zhen Kang; Pei-Pei Zhang; Ba-Sen Li; Hui-Juan You; Sheng-Chao Hou
Journal:  Chin Med J (Engl)       Date:  2018-07-20       Impact factor: 2.628

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