Abdominal Radiology

, Volume 43, Issue 11, pp 3109–3116 | Cite as

PI-RADS v2 and ADC values: is there room for improvement?

  • Eric J. Jordan
  • Charles Fiske
  • Ronald Zagoria
  • Antonio C. WestphalenEmail author



To determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2 alone.

Materials and methods

This retrospective study included 155 men whom underwent 3-Tesla prostate MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. All scans were performed with a surface coil and included T2, diffusion-weighted, and dynamic contrast-enhanced sequences. Suspicious findings were classified using Prostate Imaging Reporting and Data System (PI-RADS) v2 and targeted using MR/US fusion biopsies. Mixed-effect logistic regression analyses were used to determine the ability of PIRADS v2 alone and combined with ADC values to predict CS-PCa. As ADC categories are more practical in clinical situations than numeric values, an additional model with ADC categories of ≤ 800 and > 800 was performed.


A total of 243 suspicious lesions were included, 69 of which were CS-PCa, 34 were Gleason score 3+3 PCa, and 140 were negative. The overall PIRADS v2 score, ADC values, and ADC categories are independent statistically significant predictors of CS-PCa (p < 0.001). However, the area under the ROC of PIRADS v2 alone and PIRADS v2 with ADC categories are significantly different in both peripheral and transition zone lesions (p = 0.026 and p = 0.03, respectively) Further analysis of the ROC curves also shows that the main benefit of utilizing ADC values or categories is better discrimination of PI-RADS v2 4 lesions.


ADC values and categories help to diagnose CS-PCa when lesions are assigned a PI-RADS v2 score of 4.


Prostate MRI PI-RADS ADC values Prostate cancer 


Compliance with ethical standards


This study was not funded.

Conflict of interest

Eric J. Jordan: none. Charles Fiske: none. Ronald Zagoria: consultant for Recor Medical, Inc. Antonio C. Westphalen: scientific advisory board member for 3D Biopsy, LLC.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

IRB statement

This retrospective study was approved by the UCSF Institutional Committee for Human Research.


  1. 1.
    NCI (2017) SEER Stat Fact Sheets: Prostate Cancer. [Website] Washington, DC National Cancer Institute. 11 Oct 2017.
  2. 2.
    Siddiqui MM, Rais-Bahrami S, Truong H, et al. (2013) Magnetic resonance imaging/ultrasound–fusion biopsy significantly upgrades prostate cancer versus systematic 12-core transrectal ultrasound biopsy. Eur Urol 64(5):713–719CrossRefGoogle Scholar
  3. 3.
    Marks L, Young S, Natarajan S (2013) MRI-ultrasound fusion for guidance of targeted prostate biopsy. Curr Opin Urol 23(1):43CrossRefGoogle Scholar
  4. 4.
    Wysock JS, Rosenkrantz AB, Huang WC, et al. (2014) A prospective, blinded comparison of magnetic resonance (MR) imaging–ultrasound fusion and visual estimation in the performance of MR-targeted prostate biopsy: the PROFUS trial. Eur Urol 66(2):343–351CrossRefGoogle Scholar
  5. 5.
    Schimmoller L, Quentin M, Arsov C, et al. (2013) Inter-reader agreement of the ESUR score for prostate MRI using in-bore MRI-guided biopsies as the reference standard. Eur Radiol 23(11):3185–3190CrossRefGoogle Scholar
  6. 6.
    Siddiqui MM, Rais-Bahrami S, Turkbey B, et al. (2015) Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA 313:390–397CrossRefGoogle Scholar
  7. 7.
    Rosenkrantz AB, Kim S, Lim RP, et al. (2013) Prostate cancer localization using multiparametric MR imaging: comparison of Prostate Imaging Reporting and Data System (PI-RADS) and Likert scales. Radiology 269(2):482–492CrossRefGoogle Scholar
  8. 8.
    Portalez D, Mozer P, Cornud F, et al. (2012) Validation of the European Society of Urogenital Radiology scoring system for prostate cancer diagnosis on multiparametric magnetic resonance imaging in a cohort of repeat biopsy patients. Eur Urol 62(6):986–996CrossRefGoogle Scholar
  9. 9.
    Hamoen EH, de Rooij M, Witjes JA, Barentsz JO, Rovers MM (2015) Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol 67(6):1112–1121CrossRefGoogle Scholar
  10. 10.
    Schoots IG, Roobol MJ, Nieboer D, et al. (2015) Magnetic resonance imaging-targeted biopsy may enhance the diagnostic accuracy of significant prostate cancer detection compared to standard transrectal ultrasound guided biopsy: a systematic review and meta-analysis. Eur Urol 68(3):438–450CrossRefGoogle Scholar
  11. 11.
    Westphalen AC, Rosenkrantz AB (2014) Prostate imaging reporting and data system (PI-RADS): reflections on early experience with a standardized interpretation scheme for multiparametric prostate MRI. Am J Roentgenol 202(1):121–123CrossRefGoogle Scholar
  12. 12.
    ACR (2015) MR Prostate Imaging Reporting and Data System version 2.0. [Website] Washington, DC American College of Radiology. Accessed 10 August 2016
  13. 13.
    Steiger Philipp, Thoeny Harriet C (2016) Prostate MRI based on PI-RADS version 2: how we review and report. Cancer Imaging 16(1):1CrossRefGoogle Scholar
  14. 14.
    Vargas HA, Hötker AM, Goldman DA, et al. (2016) Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference. Eur Radiol 26(6):1606–1612CrossRefGoogle Scholar
  15. 15.
    Peng Y, Jiang Y, Yang C, et al. (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score: a computer-aided diagnosis development study. Radiology 267(3):787–796CrossRefGoogle Scholar
  16. 16.
    Donati OF, Mazaheri Y, Afaq A, et al. (2014) Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 271(1):143–152CrossRefGoogle Scholar
  17. 17.
    Peng Y, Jiang Y, Antic T, et al. (2014) Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study. Radiology 271(2):461–471CrossRefGoogle Scholar
  18. 18.
    Lebovici A, Sfrangeu SA, Feier D, et al. (2014) Evaluation of the normal-to-diseased apparent diffusion coefficient ratio as an indicator of prostate cancer aggressiveness. BMC Med Imaging 14:15CrossRefGoogle Scholar
  19. 19.
    Zhang YD, Wang Q, Wu CJ, et al. (2015) The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the Gleason grade of prostate cancer. Eur Radiol 25(4):994–1004CrossRefGoogle Scholar
  20. 20.
    Lin WC, Westphalen AC, Silva GE, et al. (2016) Comparison of PI-RADS 2, ADC histogram-derived parameters, and their combination for the diagnosis of peripheral zone prostate cancer. Abdom Radiol 41(11):2209–2217CrossRefGoogle Scholar
  21. 21.
    Merisaari H, Jambor I (2015) Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm: simulation and repeatability study. Magn Reson Med 73(5):1954–1969CrossRefGoogle Scholar
  22. 22.
    Jambor I, Merisaari H, Taimen P, et al. (2015) Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: a repeatability study. Magn Reson Med 73(5):1988–1998CrossRefGoogle Scholar
  23. 23.
    Park SY, Shin SJ, Jung DC, et al. (2016) PI-RADS version 2: quantitative analysis aids reliable interpretation of diffusion-weighted imaging for prostate cancer. Eur Radiol 12:1–8Google Scholar
  24. 24.
    Shaish Hiram, Kang Stella K, Rosenkrantz Andrew B (2017) The utility of quantitative ADC values for differentiating high-risk from low-risk prostate cancer: a systematic review and meta-analysis. Abdom Radiol 42(1):260–270CrossRefGoogle Scholar
  25. 25.
    Jordan EJ, Fiske C, Zagoria RJ, Westphalen AC (2017) Evaluating the performance of PI-RADS v2 in the non-academic setting. Abdom Radiol 42:2725–2731CrossRefGoogle Scholar
  26. 26.
    Weinreb JC, Barentsz J, Choyke PL, et al. (2016) PI-RADS prostate imaging-reporting and data system version 2. Eur Urol 69(1):16–40CrossRefGoogle Scholar
  27. 27.
    Lin WC, Westphalen AC, Silva GE, et al. (2016) Comparison of PI-RADS 2, ADC histogram-derived parameters, and their combination for the diagnosis of peripheral zone prostate cancer. Abdom Radiol 41:2209–2217CrossRefGoogle Scholar
  28. 28.
    Kasel-Seibert M, Lehmann T, Aschenbach R, et al. (2016) Assessment of PI-RADS v2 for the detection of prostate cancer. Eur J Radiol 85(4):726–731CrossRefGoogle Scholar
  29. 29.
    Mertan FV, Greer MD, Shih JH, et al. (2016) Prospective evaluation of the prostate imaging reporting and data system version 2 for prostate cancer detection. J Urol 196:690–696CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Eric J. Jordan
    • 1
  • Charles Fiske
    • 2
  • Ronald Zagoria
    • 1
  • Antonio C. Westphalen
    • 1
    • 3
    Email author
  1. 1.Department of RadiologyUniversity of California San FranciscoSan FranciscoUSA
  2. 2.NorCal Imaging, RadNetWalnut CreekUSA
  3. 3.Department of Radiology & Biomedical ImagingUniversity of California San FranciscoSan FranciscoUSA

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