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Rofo 2021; 193(07): 787-796
DOI: 10.1055/a-1324-4010
Review

PI-RADS 2.1 – Befundinterpretation: Die wichtigsten Neuerungen und ihre klinischen Implikationen

Article in several languages: English | deutsch
Thomas Beyer 
1   Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
,
Heinz-Peter Schlemmer
2   Radiology, German Cancer Research Centre, Heidelberg, Germany
,
Marc-André Weber 
1   Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
,
Kolja M. Thierfelder
1   Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
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Zusammenfassung

Hintergrund Die multiparametrische Magnetresonanztomografie (MRT) der Prostata übernimmt inzwischen eine zentrale Rolle in der Diagnostik bei Patienten mit Verdacht auf Prostatakarzinom. Zum Erfolg der Methode trägt die zunehmende Verbreitung der zuletzt im Jahr 2019 auf die Version 2.1 aktualisierte Leitlinie zur Standardisierung von Bildakquisition, Auswertung und Befundung (Prostate Imaging – Reporting and Data System, PI-RADS) bei.

Material und Methoden Die zentralen Neuerungen der im Frühjahr 2019 vorgestellten PI-RADS-Version 2.1 gegenüber der Vorgängerversion PI-RADS 2.0 werden vorgestellt und bezüglich ihrer klinischen Implikationen diskutiert.

Ergebnisse Die PI-RADS-Version 2.1 zielt darauf ab, die Anwendung zu vereinfachen, ohne dabei das Grundkonzept der dominanten Sequenz (DWI in der peripheren Zone, T2 in der Transitionszone) zu ändern. Von besonderer Bedeutung sind die stärkere Gewichtung der Diffusionsbildgebung in der Transitionszone, der nun obligatorische hohe b-Wert von mindestens 1400 s/mm2 und neue Hinweise zur Beurteilung der zentralen Zone und des anterioren fibromuskulären Stromas.

Schlussfolgerung Die 2019 veröffentlichte PI-RADS-Version 2.1 adressiert verschiedene Änderungen, die sowohl Untersuchungstechnik als auch Befundung betreffen. Inwieweit sich damit die Ziele der Reduktion der Interreader-Variabilität und der Erhöhung der Detektionsrate in der Transitionszone erreichen lassen, müssen prospektive klinische Studien zeigen.

Kernaussagen: 

  • Die neue PI-RADS-Version 2.1 umfasst einzelne Änderungen bezüglich Befundinterpretation und Untersuchungstechnik.

  • In der Transitionszone wird die Rolle der Diffusionsbildgebung gestärkt.

  • Ein ultrahoher b-Wert von mindestens 1400 s/mm2 ist nach PI-RADS 2.1 obligat.

  • Die biparametrische MRT wird nicht zur generellen Anwendung empfohlen.

Zitierweise

  • Beyer T, Schlemmer H, Weber M et al. PI-RADS 2.1 – Image Interpretation: The Most Important Updates and Their Clinical Implications. Fortschr Röntgenstr 2021; 193: 787 – 796

Key words

prostate - PI-RADS - prostate cancer - uroradiology - MR imaging


Publication History

Received: 04 March 2020

Accepted: 10 November 2020

Article published online:
21 December 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Bray F, Ferlay J, Soerjomataram I. et al Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68: 394-424
    CrossrefPubMedSearch in Google Scholar
  • 2 Lam TBL, MacLennan S, Willemse PM. et al EAU-EANM-ESTRO-ESUR-SIOG Prostate Cancer Guideline Panel Consensus Statements for Deferred Treatment with Curative Intent for Localised Prostate Cancer from an International Collaborative Study (DETECTIVE Study). Eur Urol 2019; 76: 790-813
    CrossrefPubMedSearch in Google Scholar
  • 3 Kim R, Kim CK, Park JJ. et al Prognostic Significance for Long-Term Outcomes Following Radical Prostatectomy in Men with Prostate Cancer: Evaluation with Prostate Imaging Reporting and Data System Version 2. Korean J Radiol 2019; 20: 256-264
    CrossrefPubMedSearch in Google Scholar
  • 4 Schlemmer HP. Multiparametrische MR-Bildgebung beim Prostatakarzinom. Radiologie up2date 2017; 17: 43-60
    Thieme ConnectPubMedSearch in Google Scholar
  • 5 Barentsz JO, Richenberg J, Clements R. et al ESUR prostate MR guidelines 2012. Eur Radiol 2012; 22: 746-757
    CrossrefPubMedSearch in Google Scholar
  • 6 Weinreb JC, Barentsz JO, Choyke PL. et al PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. Eur Urol 2016; 69: 16-40
    CrossrefPubMedSearch in Google Scholar
  • 7 Rosenkrantz AB, Ginocchio LA, Cornfeld D. et al Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists. Radiology 2016; 280: 793-804
    CrossrefPubMedSearch in Google Scholar
  • 8 Borofsky S, George AK, Gaur S. et al What Are We Missing? False-Negative Cancers at Multiparametric MR Imaging of the Prostate. Radiology 2018; 286: 186-195
    CrossrefPubMedSearch in Google Scholar
  • 9 Kido A, Tamada T, Kanomata N. et al Multidimensional analysis of clinicopathological characteristics of false-negative clinically significant prostate cancers on multiparametric MRI of the prostate in Japanese men. Jpn J Radiol 2019; 37: 154-164
    CrossrefPubMedSearch in Google Scholar
  • 10 Muller BG, Shih JH, Sankineni S. et al Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging. Radiology 2015; 277: 741-750
    CrossrefPubMedSearch in Google Scholar
  • 11 Rosenkrantz AB, Oto A, Turkbey B. et al Prostate Imaging Reporting and Data System (PI-RADS), Version 2: A Critical Look. Am J Roentgenol 2016; 206: 1179-1183
    CrossrefPubMedSearch in Google Scholar
  • 12 Turkbey B, Rosenkrantz AB, Haider MA. et al Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019; 76: 340-351
    CrossrefPubMedSearch in Google Scholar
  • 13 Barrett T, Rajesh A, Rosenkrantz AB. et al PI-RADS version 2.1: one small step for prostate MRI. Clin Radiol 2019; 74: 841-852
    CrossrefPubMedSearch in Google Scholar
  • 14 Le Bihan D, Breton E, Lallemand D. et al Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168: 497-505
    CrossrefPubMedSearch in Google Scholar
  • 15 Barrett T, Priest AN, Lawrence EM. et al Ratio of Tumor to Normal Prostate Tissue Apparent Diffusion Coefficient as a Method for Quantifying DWI of the Prostate. Am J Roentgenol 2015; 205: W585-W593
    CrossrefPubMedSearch in Google Scholar
  • 16 Jensen JH, Helpern JA, Ramani A. et al Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53: 1432-1440
    CrossrefPubMedSearch in Google Scholar
  • 17 Rosenkrantz AB, Sigmund EE, Johnson G. et al Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 2012; 264: 126-135
    CrossrefPubMedSearch in Google Scholar
  • 18 DeLano MC, Cooper TG, Siebert JE. et al High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 2000; 21: 1830-1836
    Search in Google Scholar
  • 19 Kim CK, Park BK, Kim B. High-b-value diffusion-weighted imaging at 3 T to detect prostate cancer: comparisons between b values of 1000 and 2000 s/mm2 . Am J Roentgenol 2010; 194: W33-W37
    CrossrefPubMedSearch in Google Scholar
  • 20 Agarwal HK, Mertan FV, Sankineni S. et al Optimal high b-value for diffusion weighted MRI in diagnosing high risk prostate cancers in the peripheral zone. J Magn Reson Imaging 2017; 45: 125-131
    CrossrefPubMedSearch in Google Scholar
  • 21 Syer TJ, Godley KC, Cameron D. et al The diagnostic accuracy of high b-value diffusion- and T2-weighted imaging for the detection of prostate cancer: a meta-analysis. Abdom Radiol (NY) 2018; 43: 1787-1797
    CrossrefPubMedSearch in Google Scholar
  • 22 Metens T, Miranda D, Absil J. et al What is the optimal b value in diffusion-weighted MR imaging to depict prostate cancer at 3T. Eur Radiol 2012; 22: 703-709
    CrossrefPubMedSearch in Google Scholar
  • 23 Ohgiya Y, Suyama J, Seino N. et al Diagnostic accuracy of ultra-high-b-value 3.0-T diffusion-weighted MR imaging for detection of prostate cancer. Clin Imaging 2012; 36: 526-531
    CrossrefPubMedSearch in Google Scholar
  • 24 Jambor I. Optimization of prostate MRI acquisition and post-processing protocol: a pictorial review with access to acquisition protocols. Acta Radiol Open 2017; 6: 2058460117745574
    PubMedSearch in Google Scholar
  • 25 Panebianco V, Giganti F, Kitzing YX. et al An update of pitfalls in prostate mpMRI: a practical approach through the lens of PI-RADS v. 2 guidelines. Insights Imaging 2018; 9: 87-101
    CrossrefPubMedSearch in Google Scholar
  • 26 Ream JM, Doshi AM, Dunst D. et al Dynamic contrast-enhanced MRI of the prostate: An intraindividual assessment of the effect of temporal resolution on qualitative detection and quantitative analysis of histopathologically proven prostate cancer. J Magn Reson Imaging 2017; 45: 1464-1475
    CrossrefPubMedSearch in Google Scholar
  • 27 Boesen L, Nørgaard N, Løgager V. et al Assessment of the Diagnostic Accuracy of Biparametric Magnetic Resonance Imaging for Prostate Cancer in Biopsy-Naive Men: The Biparametric MRI for Detection of Prostate Cancer (BIDOC) Study. JAMA Netw Open 2018; 1: e180219
    CrossrefPubMedSearch in Google Scholar
  • 28 Jambor I, Boström PJ, Taimen P. et al Novel biparametric MRI and targeted biopsy improves risk stratification in men with a clinical suspicion of prostate cancer (IMPROD Trial). J Magn Reson Imaging 2017; 46: 1089-1095
    CrossrefPubMedSearch in Google Scholar
  • 29 Gupta RT, Mehta KA, Turkbey B. et al PI-RADS: Past, present, and future. J Magn Reson Imaging 2019;
    CrossrefPubMedSearch in Google Scholar
  • 30 Greer MD, Shih JH, Lay N. et al Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2. Radiology 2017; 285: 859-869
    CrossrefPubMedSearch in Google Scholar
  • 31 Krishna S, McInnes M, Lim C. et al Comparison of Prostate Imaging Reporting and Data System versions 1 and 2 for the Detection of Peripheral Zone Gleason Score 3 + 4 = 7 Cancers. Am J Roentgenol 2017; 209: W365-W373
    CrossrefPubMedSearch in Google Scholar
  • 32 Rosenkrantz AB, Babb JS, Taneja SS. et al Proposed Adjustments to PI-RADS Version 2 Decision Rules: Impact on Prostate Cancer Detection. Radiology 2017; 283: 119-129
    CrossrefPubMedSearch in Google Scholar
  • 33 Caglic I, Barrett T. Optimising prostate mpMRI: prepare for success. Clin Radiol 2019; 74: 831-840
    CrossrefPubMedSearch in Google Scholar
  • 34 Iwazawa J, Mitani T, Sassa S. et al Prostate cancer detection with MRI: is dynamic contrast-enhanced imaging necessary in addition to diffusion-weighted imaging. Diagn Interv Radiol 2011; 17: 243-248
    CrossrefPubMedSearch in Google Scholar
  • 35 Padhani AR, Weinreb J, Rosenkrantz AB. et al Prostate Imaging-Reporting and Data System Steering Committee: PI-RADS v2 Status Update and Future Directions. Eur Urol 2019; 75: 385-396
    CrossrefPubMedSearch in Google Scholar
  • 36 Scialpi M, Martorana E, Scialpi P. et al Round table: arguments in supporting abbreviated or biparametric MRI of the prostate protocol. Abdom Radiol (NY) 2020;
    CrossrefPubMedSearch in Google Scholar
  • 37 Scialpi M, Aisa MC, D‘Andrea A. et al Simplified Prostate Imaging Reporting and Data System for Biparametric Prostate MRI: A Proposal. Am J Roentgenol 2018; 211: 379-382
    CrossrefPubMedSearch in Google Scholar
  • 38 Vargas HA, Akin O, Franiel T. et al Normal central zone of the prostate and central zone involvement by prostate cancer: clinical and MR imaging implications. Radiology 2012; 262: 894-902
    CrossrefPubMedSearch in Google Scholar
  • 39 Hansford BG, Karademir I, Peng Y. et al Dynamic contrast-enhanced MR imaging features of the normal central zone of the prostate. Acad Radiol 2014; 21: 569-577
    CrossrefPubMedSearch in Google Scholar
  • 40 Cohen RJ, Shannon BA, Phillips M. et al Central zone carcinoma of the prostate gland: a distinct tumor type with poor prognostic features. J Urol 2008; 179: 1762-1767 ; discussion 1767.
    PubMedSearch in Google Scholar
  • 41 Ward E, Baad M, Peng Y. et al Multi-parametric MR imaging of the anterior fibromuscular stroma and its differentiation from prostate cancer. Abdom Radiol (NY) 2017; 42: 926-934
    CrossrefPubMedSearch in Google Scholar
  • 42 Bouyé S, Potiron E, Puech P. et al Transition zone and anterior stromal prostate cancers: zone of origin and intraprostatic patterns of spread at histopathology. Prostate 2009; 69: 105-113
    CrossrefPubMedSearch in Google Scholar
  • 43 McNeal JE, Redwine EA, Freiha FS. et al Zonal distribution of prostatic adenocarcinoma. Correlation with histologic pattern and direction of spread. Am J Surg Pathol 1988; 12: 897-906
    CrossrefPubMedSearch in Google Scholar
  • 44 Kayhan A, Fan X, Oommen J. et al Multi-parametric MR imaging of transition zone prostate cancer: Imaging features, detection and staging. World J Radiol 2010; 2: 180-187
    CrossrefPubMedSearch in Google Scholar
  • 45 Tamada T, Kido A, Takeuchi M. et al Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer. Eur J Radiol 2019; 121: 108704
    CrossrefPubMedSearch in Google Scholar
  • 46 Søndergaard G, Vetner M, Christensen PO. Periferal cystic hyperplasia of the prostate gland. Acta Pathol Microbiol Immunol Scand A 1987; 95: 137-139
    PubMedSearch in Google Scholar
  • 47 Hansen NL, Barrett T, Koo B. et al The influence of prostate-specific antigen density on positive and negative predictive values of multiparametric magnetic resonance imaging to detect Gleason score 7–10 prostate cancer in a repeat biopsy setting. BJU Int 2017; 119: 724-730
    CrossrefPubMedSearch in Google Scholar
  • 48 Bonekamp D, Kohl S, Wiesenfarth M. et al Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology 2018; 289: 128-137
    CrossrefPubMedSearch in Google Scholar
  • 49 Kaufmann S, Bedke J, Gatidis S. et al Prostate cancer gene 3 (PCA3) is of additional predictive value in patients with PI-RADS grade III (intermediate) lesions in the MR-guided re-biopsy setting for prostate cancer. World J Urol 2016; 34: 509-515
    CrossrefPubMedSearch in Google Scholar