Advertisement

European Radiology

, Volume 29, Issue 4, pp 1820–1830 | Cite as

Histopathological to multiparametric MRI spatial mapping of extended systematic sextant and MR/TRUS-fusion-targeted biopsy of the prostate

  • David BonekampEmail author
  • Patrick Schelb
  • Manuel Wiesenfarth
  • Tristan Anselm Kuder
  • Fenja Deister
  • Albrecht Stenzinger
  • Joanne Nyarangi-Dix
  • Matthias Röthke
  • Markus Hohenfellner
  • Heinz-Peter Schlemmer
  • Jan Philipp Radtke
Urogenital
  • 156 Downloads

Abstract

Purpose

MRI has limited ability to detect multifocal disease or the full extent of prostate involvement with clinically significant prostate cancer (sPC). We compare the spatial co-localization at sextant resolution of MRI lesions and histopathological mapping by combined targeted and extended systematic biopsies.

Materials and methods

Sextants were mapped for sPC (ISUP group ≥ 2) by 24-core transperineal systematic biopsies in 316 patients with suspicion for sPC and by MR lesions of PI-RADS score of ≥ 3. The gold standard is combined systematic (median 23 cores) and targeted biopsies.

Results

Of 316 men, 121 (38%) harbored sPC. Of these 121 patients, 4 (3%) had a negative MRI. MRI correctly identified 117/121 (97%) patients with sPC. In these patients, mpMRI missed no additional sPC in 96 (82%), while MRI-negative sPC lesions were present in 21 patients (18%). Of 1896 sextants, 379 (20%) harbored sPC. MR-positive sextants contained sPC in 26% (337/1275), compared to 7% (42/621) in MR-negative sextants. On a patient basis, sensitivity was 0.97, specificity 0.22, positive predictive value 0.43, and negative predictive value 0.91. On a sextant basis, sensitivity was 0.73, specificity 0.38, positive predictive value 0.26, and negative predictive value 0.93.

Conclusion

MpMRI mapping agreed well with histopathology with, at the observed sPC prevalence and on a patient basis, excellent sensitivity and negative predictive value, and acceptable specificity and positive predictive value for sPC. However, 18% of sPC was outside the mpMRI mapped region, quantifying limitations of MRI for complete localization of disease extent.

Key Points

• Currently, exclusive MRI mapping of the prostate for focal treatment planning cannot be recommended, as significant prostate cancer may remain untreated in a substantial number of cases.

• At the observed sPC prevalence and on a patient basis, mpMRI has excellent sensitivity and NPV, and acceptable specificity and PPV for detection of prostate cancer, supporting its use to detect suspicious lesions before biopsy.

• Despite the excellent global performance, 18% of sPC was outside the mpMRI mapped region even when a security margin of 10 mm was considered, indicating that prostate MRI has limited ability to completely map all cancer foci within the prostate.

Keywords

Male Prostate cancer Magnetic resonance imaging Image-guided biopsy 

Abbreviations

AS

Active surveillance

Bx

Biopsy

DCE

Dynamic contrast-enhanced imaging

DRE

Digital-rectal examination

DWI

Diffusion-weighted imaging

EPI

Echo-planar imaging

GP

Gleason pattern

GS

Gleason score

ISUP

International Society of Urological Pathology

mpMRI

Multiparametric magnetic resonance imaging

MRI

Magnetic resonance imaging

NPV

Negative predictive value

PC

Prostate cancer

PI-RADS

Prostate Imaging Reporting and Data System

PPV

Positive predictive value

PSA

Prostate specific antigen

RP

Radical prostatectomy

SB

Systematic transperineal saturation

sPC

Significant prostate cancer

STARD

Standards of Reporting of Diagnostic Accuracy

START

Standards of Reporting for MRI-targeted Biopsy Studies

TB

MRI-targeted biopsy

TRUS

Transrectal ultrasound

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Heinz-Peter Schlemmer.

Conflict of interest

David Bonekamp is a speaker for Profound Medical Inc.

Patrick Schelb has nothing to declare.

Manuel Wiesenfarth has nothing to declare.

Tristan Anselm Kuder has nothing to declare.

Fenja Deister has nothing to declare.

Albrecht Stenzinger declares the following: consulting fee and payment for lectures: Astra Zeneca, BMS, Novartis, Roche, Illumina, Thermo Fisher; travel support: Astra Zeneca, BMS, Novartis, Illumina, Thermo Fisher; board member: Astra Zeneca, BMS, Novartis, Thermo Fisher.

Joanne Nyarangi-Dix has nothing to declare.

Matthias Röthke declares consulting fee and payment for lectures: Siemens Healthineers, Curagita AG.

Markus Hohenfellner has nothing to declare.

Heinz-Peter Schlemmer declares the following: consulting fee or honorarium: Siemens, Curagita, Profound, Bayer; travel support: Siemens, Curagita, Profound, Bayer; board member: Curagita; consultancy: Curagita, Bayer; grants/grants pending: BMBF, Deutsche Krebshilfe, Dietmar-Hopp-Stiftung, Roland-Ernst-Stiftung; payment for lectures: Siemens, Curagita, Profound, Bayer.

Jan Philipp Radtke declares payment for consultant work from Saegeling Medizintechnik and Siemens Heathineers and for development of educational presentations from Saegeling Medizintechnik.

Statistics and biometry

Manuel Wiesenfarth is the lead statistician and co-author on this paper.

Informed consent

Written informed consent was waived by the Ethics Commission.

Ethical approval

Ethical approval was obtained.

Study subjects or cohorts overlap

The examined cohort was subject to a recently published study (Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions by MRI: comparison to ADC values. Radiology 31:173064.  https://doi.org/10.1148/radiol.2018173064. Focusing on apparent diffusion coefficient and radiomics for lesion classification; however, sextant-level histopathology to mpMRI mapping has not been previously performed.

Methodology

• retrospective

• diagnostic study

• single-center study

Supplementary material

330_2018_5751_MOESM1_ESM.docx (10.9 mb)
ESM 1 (DOCX 11115 kb)
330_2018_5751_MOESM2_ESM.docx (21 kb)
ESM 2 (DOCX 20 kb)

References

  1. 1.
    Panebianco V, Barchetti G, Simone G et al (2018) Negative multiparametric magnetic resonance imaging for prostate cancer : what’s next ? Eur Urol.  https://doi.org/10.1016/j.eururo.2018.03.007
  2. 2.
    Barentsz JO, Richenberg J, Clements R et al (2012) ESUR prostate MR guidelines 2012. Eur Radiol 22:746–757.  https://doi.org/10.1007/s00330-011-2377-y CrossRefGoogle Scholar
  3. 3.
    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 64:713–719.  https://doi.org/10.1016/j.eururo.2013.05.059 CrossRefGoogle Scholar
  4. 4.
    Radtke JP, Kuru TH, Boxler S et al (2015) Comparative analysis of transperineal template saturation prostate biopsy versus magnetic resonance imaging targeted biopsy with magnetic resonance imaging-ultrasound fusion guidance. J Urol.  https://doi.org/10.1016/j.juro.2014.07.098
  5. 5.
    Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate Cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 71:618–629.  https://doi.org/10.1016/j.eururo.2016.08.003 CrossRefGoogle Scholar
  6. 6.
    Kasivisvanathan V, Dufour R, Moore CM et al (2013) Transperineal magnetic resonance image targeted prostate biopsy versus transperineal template prostate biopsy in the detection of clinically significant prostate cancer. J Urol 189:860–866.  https://doi.org/10.1016/j.juro.2012.10.009 CrossRefGoogle Scholar
  7. 7.
    Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 6736:32401–32401.  https://doi.org/10.1016/S0140-6736(16)32401-1 Google Scholar
  8. 8.
    Kasivisvanathan V, Rannikko AS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med.  https://doi.org/10.1056/NEJMoa1801993
  9. 9.
    Kenigsberg AP, Llukani E, Deng FM, Melamed J, Zhou M, Lepor H (2018) The use of magnetic resonance imaging to predict oncological control among candidates for focal ablation of prostate cancer. Urology 112:121–125.  https://doi.org/10.1016/j.urology.2017.10.014
  10. 10.
    Moldovan PC, Van den Broeck T, Sylvester R et al (2017) What is the negative predictive value of multiparametric magnetic resonance imaging in excluding prostate cancer at biopsy ? A systematic review and meta-analysis from the european association of urology prostate cancer guidelines panel. Eur Urol.  https://doi.org/10.1016/j.eururo.2017.02.026
  11. 11.
    Borofsky S, George AK, Gaur S et al (2018) What are we missing ? False-negative cancers at multiparametric MR imaging of the prostate 1. Radiology 0:1–10.  https://doi.org/10.1148/radiol.2017152877 Google Scholar
  12. 12.
    Radtke JP, Schwab C, Wolf MB et al (2016) Multiparametric magnetic resonance imaging (MRI) and MRI – transrectal ultrasound fusion biopsy for index tumor detection : correlation with radical prostatectomy specimen. Eur Urol 70:846–853.  https://doi.org/10.1016/j.eururo.2015.12.052 CrossRefGoogle Scholar
  13. 13.
    Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions by MRI: comparison to ADC values. Radiology.  https://doi.org/10.1148/radiol.2018173064
  14. 14.
    Moore CM, Kasivisvanathan V, Eggener S et al (2013) Standards of reporting for MRI-targeted biopsy studies (START) of the prostate: recommendations from an International Working Group. Eur Urol 64:544–552.  https://doi.org/10.1016/j.eururo.2013.03.030 CrossRefGoogle Scholar
  15. 15.
    Weinreb JC, Barentsz JO, Choyke PL et al (2015) PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur Urol 69(1):16–40.  https://doi.org/10.1016/j.eururo.2015.08.052
  16. 16.
    Kuru TH, Wadhwa K, Chang RT et al (2013) Definitions of terms, processes and a minimum dataset for transperineal prostate biopsies: a standardization approach of the Ginsburg Study Group for Enhanced Prostate Diagnostics. BJU Int 112:568–577.  https://doi.org/10.1111/bju.12132 CrossRefGoogle Scholar
  17. 17.
    Nolden M, Zelzer S, Seitel A et al (2013) The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620.  https://doi.org/10.1007/s11548-013-0840-8 CrossRefGoogle Scholar
  18. 18.
    Fritzsche KH, Neher PF, Reicht I et al (2012) MITK diffusion imaging. Methods Inf Med 51:441–448.  https://doi.org/10.3414/ME11-02-0031 CrossRefGoogle Scholar
  19. 19.
    Le Nobin J, Rosenkrantz AB, Villers A et al (2015) Image guided focal therapy of MRI-visible prostate cancer: defining a 3D treatment margin based on MRI-histology co-registration analysis. J Urol 1–7.  https://doi.org/10.1016/j.juro.2015.02.080
  20. 20.
    R Development Core Team R (2015) R: a language and environment for statistical computing. Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed 12 Aug 2018
  21. 21.
    Bossuyt PM, Reitsma JB, Bruns DE et al (2003) Towards complete and, accurate reporting of studies of diagnostic accuracy: the STARD initiative. Radiology 226:24–28.  https://doi.org/10.1136/bmj.326.7379.41 CrossRefGoogle Scholar
  22. 22.
    Baco E, Ukimura O, Rud E et al (2015) Magnetic resonance imaging – transectal ultrasound image-fusion biopsies accurately characterize the index tumor: correlation with step-sectioned radical prostatectomy specimens in 135 patients. Eur Urol 67:787–794.  https://doi.org/10.1016/j.eururo.2014.08.077 CrossRefGoogle Scholar
  23. 23.
    Mortezavi A, Märzendorfer O, Donati OF et al (2018) Diagnostic accuracy of mpMRI and fusion-guided targeted biopsy evaluated by transperineal template saturation prostate biopsy for the detection and characterization of prostate cancer. J Urol.  https://doi.org/10.1016/j.juro.2018.02.067
  24. 24.
    De Visschere PJ, Naesens L, Libbrecht L et al (2016) What kind of prostate cancers do we miss on multiparametric magnetic resonance imaging? Eur Radiol 26:1098–1107.  https://doi.org/10.1007/s00330-015-3894-x CrossRefGoogle Scholar
  25. 25.
    Ullrich T, Quentin M, Arsov C et al (2017) Risk stratification of “equivocal” PI-RADS lesions in mp-MRI of the prostate. J Urol.  https://doi.org/10.1016/j.juro.2017.09.074
  26. 26.
    Meng X, Rosenkrantz AB, Mendhiratta N et al (2016) Relationship of pre-biopsy multiparametric MRI and biopsy indication with MRI-US fusion-targeted prostate biopsy outcomes. Eur Urol 69:512–517.  https://doi.org/10.1016/j.eururo.2015.06.005 CrossRefGoogle Scholar
  27. 27.
    Venderink W, Van Luijtelaar A, Bomers JG et al (2017) Results of targeted biopsy in men with magnetic resonance imaging lesions classified equivocal , likely or highly Likely to be clinically significant prostate cancer. Eur Urol 1–8.  https://doi.org/10.1016/j.eururo.2017.02.021
  28. 28.
    Le Nobin J, Orczyk C, Deng FM et al (2014) Prostate tumour volumes: evaluation of the agreement between magnetic resonance imaging and histology using novel co-registration software. BJU Int 114:E105–E112.  https://doi.org/10.1111/bju.12750 CrossRefGoogle Scholar
  29. 29.
    Bratan F, Melodelima C, Souchon R et al (2015) How accurate is multiparametric MR imaging in evaluation of prostate cancer volume? Radiology 275:144–154.  https://doi.org/10.1148/radiol.14140524 CrossRefGoogle Scholar
  30. 30.
    Sonn GA, Fan RE, Ghanouni P et al (2017) Prostate magnetic resonance imaging interpretation varies substantially across radiologists. Eur Urol Focus.  https://doi.org/10.1016/j.euf.2017.11.010
  31. 31.
    Turkbey B, Pinto PA, Mani H et al (2010) Prostate cancer: value of multiparametric MR imaging at 3 T for detection--histopathologic correlation. Radiology 255:89–99.  https://doi.org/10.1148/radiol.09090475 CrossRefGoogle Scholar
  32. 32.
    Rosenkrantz AB, Deng FM, Kim S et al (2012) Prostate cancer: multiparametric mri for index lesion localization - a multiple-reader study. AJR Am J Roentgenol 199:830–837.  https://doi.org/10.2214/AJR.11.8446 CrossRefGoogle Scholar
  33. 33.
    Dickinson L, Ahmed HU, Allen C et al (2011) Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a European consensus meeting. Eur Urol 59:477–494.  https://doi.org/10.1016/j.eururo.2010.12.009 CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • David Bonekamp
    • 1
    Email author
  • Patrick Schelb
    • 1
  • Manuel Wiesenfarth
    • 2
  • Tristan Anselm Kuder
    • 3
  • Fenja Deister
    • 3
  • Albrecht Stenzinger
    • 4
  • Joanne Nyarangi-Dix
    • 5
  • Matthias Röthke
    • 1
  • Markus Hohenfellner
    • 5
  • Heinz-Peter Schlemmer
    • 1
  • Jan Philipp Radtke
    • 1
    • 5
  1. 1.Department of RadiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
  2. 2.Division of Statistics, German Cancer Research Center (DKFZ)HeidelbergGermany
  3. 3.Department of Medical PhysicsGerman Cancer Research Center (DKFZ)HeidelbergGermany
  4. 4.Institute of PathologyUniversity of Heidelberg Medical CenterHeidelbergGermany
  5. 5.Department of UrologyUniversity of Heidelberg Medical CenterHeidelbergGermany

Personalised recommendations