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European Radiology

, Volume 30, Issue 1, pp 26–37 | Cite as

Prediction of extraprostatic extension on multi-parametric magnetic resonance imaging in patients with anterior prostate cancer

  • Hyungwoo Ahn
  • Sung Il HwangEmail author
  • Hak Jong Lee
  • Hyoung Sim Suh
  • Gheeyoung Choe
  • Seok-Soo Byun
  • Sung Kyu Hong
  • Sangchul Lee
  • Joongyub Lee
Urogenital

Abstract

Objectives

To validate how established markers of extraprostatic extension (EPE) are applied to anterior prostate cancers (APCs), and to investigate other novel markers if available.

Methods

Among 614 histopathologically confirmed APCs from 2011 to 2016, 221 lesions with PiRADS (verion 2) scores ≥ 4 on 3-T multi-parametric MRI were analyzed retrospectively. Two radiologists independently assessed capsular morphology qualitatively with 5-point scale (normal, thinning, bulging, loss, extracapsular disease), and capsule contact length (arc), tumor dimension, and their ratio (arc-dimension ratio) quantitatively. Reproducibility in measurement was assessed with κ and intra-class correlation coefficients (ICCs). Logistic regression analysis was done to find meaningful indicators of EPE. Diagnostic performance of markers was compared to one another with generalized linear model and multi-reader multi-case ROC analysis.

Results

Reproducibility was moderate to substantial (κ 0.45–0.73) for qualitative, and moderate to almost perfect (ICC 0.50–0.87) for quantitative features of EPE. Capsular morphology (odds ratio [OR] 1.818), capsule contact length (OR 1.115), tumor dimension (OR 1.035), and arc-dimension ratio (OR 1.846) were independently associated with EPE (p ≤ 0.019). Capsular bulging and capsule contact length of 10 mm as thresholds of EPE demonstrated sensitivity/specificity of 0.58/0.85 and 0.77/0.68, respectively. Capsule contact length yielded greatest AUC (0.784), followed by capsular morphology (0.778), arc-dimension ratio (0.749), and tumor dimension (0.741). Diagnostic performance of capsular morphology, capsule contact length, and arc-dimension ratio was comparable in predicting EPE.

Conclusions

Existing markers of EPE applicable regardless of locations of tumors apply similarly to APCs. Arc-dimension ratio may be a novel marker of EPE of APCs.

Key Points

• Existing imaging markers of extraprostatic extension (EPE) which have been applied regardless of locations of tumors are reflected similarly to anterior prostate cancers (APCs).

• Measuring tumor dimension without capsular assessment may result in insufficient pre-operative prediction of EPE of APCs.

• Arc-dimension ratio (capsule contact length divided by tumor dimension) exhibited highest OR and comparable performance to existing features in predicting EPE of APCs.

Keywords

Prostatic neoplasms Adenocarcinoma Neoplasm staging Magnetic resonance imaging 

Abbreviations

ADC

Apparent diffusion coefficient

AFMS

Anterior fibromuscular stroma

APC

Anterior prostate cancer

AUC

Area under the curve

EPE

Extraprostatic extension

GS

Gleason score

ICC

Intra-class correlation coefficient

MRI

Magnetic resonance imaging

MRMC

Multi-reader, multi-case

OR

Odds ratio

pAUC

Partial AUC

PiRADS

Prostate Imaging Reporting and Data System

PSA

Prostate-specific antigen

ROC

Receiver operating characteristic

RP

Radical prostatectomy

T2W

T2-weighted

Notes

Acknowledgments

This study was presented in the annual meeting of European Congress of Radiology of 2018.

Funding

This work was supported by the National Research Foundation funded by the Ministry of Science, Information and Communications Technology, Republic of Korea (Grant No. NRF-2013R1A1A2011398).

This work was supported by the Seoul National University Bundang Hospital Research Fund (Grant No. 11-2010-008).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Hak Jong Lee.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Joongyub Lee kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Fine SW, Al-Ahmadie HA, Gopalan A, Tickoo SK, Scardino PT, Reuter VE (2007) Anatomy of the anterior prostate and extraprostatic space: a contemporary surgical pathology analysis. Adv Anat Pathol 14:401–407Google Scholar
  2. 2.
    Kang YJ, Abalajon MJ, Jang WS et al (2016) Association of anterior and lateral extraprostatic extensions with base-positive resection margins in prostate cancer. PLoS One 11:e0158922PubMedPubMedCentralGoogle Scholar
  3. 3.
    Montironi R, van der Kwast T, Boccon-Gibod L, Bono AV, Boccon-Gibod L (2003) Handling and pathology reporting of radical prostatectomy specimens. Eur Urol 44:626–636PubMedGoogle Scholar
  4. 4.
    Epstein JI, Amin M, Boccon-Gibod L et al (2005) Prognostic factors and reporting of prostate carcinoma in radical prostatectomy and pelvic lymphadenectomy specimens. Scand J Urol Nephrol Suppl 39:34–63Google Scholar
  5. 5.
    Magi-Galluzzi C, Evans AJ, Delahunt B et al (2011) International Society of Urological Pathology (ISUP) consensus conference on handling and staging of radical prostatectomy specimens. Working group 3: extraprostatic extension, lymphovascular invasion and locally advanced disease. Mod Pathol 24:26–38PubMedGoogle Scholar
  6. 6.
    Mills SE (2012) Histology for pathologists, 4th edn. Wolters Kluwer Health/Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  7. 7.
    Kiyoshima K, Yokomizo A, Yoshida T et al (2004) Anatomical features of periprostatic tissue and its surroundings: a histological analysis of 79 radical retropubic prostatectomy specimens. Jpn J Clin Oncol 34:463–468PubMedGoogle Scholar
  8. 8.
    Gaunay GS, Patel V, Shah P et al (2017) Multi-parametric MRI of the prostate: factors predicting extracapsular extension at the time of radical prostatectomy. Asian J Urol 4:31–36PubMedGoogle Scholar
  9. 9.
    Krishna S, Lim CS, McInnes MDF et al (2018) Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer. J Magn Reson Imaging 47:176–185PubMedGoogle Scholar
  10. 10.
    Feng TS, Sharif-Afshar AR, Smith SC et al (2015) Multiparametric magnetic resonance imaging localizes established extracapsular extension of prostate cancer. Urol Oncol 33:109 e15–109 e22Google Scholar
  11. 11.
    Verma S, Rajesh A (2011) A clinically relevant approach to imaging prostate cancer: review. AJR Am J Roentgenol 196:S1–10 Quiz S11–4PubMedGoogle Scholar
  12. 12.
    Rosenkrantz AB, Shanbhogue AK, Wang A, Kong MX, Babb JS, Taneja SS (2016) Length of capsular contact for diagnosing extraprostatic extension on prostate MRI: assessment at an optimal threshold. J Magn Reson Imaging 43:990–997Google Scholar
  13. 13.
    Kongnyuy M, Sidana A, George AK et al (2017) Tumor contact with prostate capsule on magnetic resonance imaging: a potential biomarker for staging and prognosis. Urol Oncol 35:30 e1–30 e8Google Scholar
  14. 14.
    Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS prostate imaging – reporting and data system: 2015, version 2. Eur Urol 69:16–40PubMedPubMedCentralGoogle Scholar
  15. 15.
    American College of Radiology (2019) Prostate Imaging-Reporting and Data System Version 2.1. https://www.acr.org/-/media/ACR/Files/RADS/Pi-RADS/PIRADS-V2-1.pdf?la=en. Accessed 2 May 2019
  16. 16.
    Akin O, Sala E, Moskowitz CS et al (2006) Transition zone prostate cancers: features, detection, localization, and staging at endorectal MR imaging. Radiology 239:784–792Google Scholar
  17. 17.
    Hoffmann R, Logan C, O'Callaghan M, Gormly K, Chan K, Foreman D (2018) Does the prostate imaging-reporting and data system (PI-RADS) version 2 improve accuracy in reporting anterior lesions on multiparametric magnetic resonance imaging (mpMRI)? Int Urol Nephrol 50:13–19PubMedGoogle Scholar
  18. 18.
    Schieda N, Lim CS, Idris M et al (2017) MRI assessment of pathological stage and surgical margins in anterior prostate cancer (APC) using subjective and quantitative analysis. J Magn Reson Imaging 45:1296–1303PubMedGoogle Scholar
  19. 19.
    Koppie TM, Bianco FJ Jr, Kuroiwa K et al (2006) The clinical features of anterior prostate cancers. BJU Int 98:1167–1171PubMedCentralGoogle Scholar
  20. 20.
    Mai KT, Moazin M, Morash C, Collins JP (2001) Transitional zone and anterior peripheral zone of the prostate. A correlation of smallvolume cancer in the biopsy cores and high psa with positive anterior margins in radical prostatectomy specimens. Urol Int 66:191–196PubMedGoogle Scholar
  21. 21.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159PubMedPubMedCentralGoogle Scholar
  22. 22.
    Vatcheva KP, Lee M, McCormick JB, Rahbar MH (2016) Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiol (Sunnyvale) 6:227Google Scholar
  23. 23.
    Dormann CF, Elith J, Sven B et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46Google Scholar
  24. 24.
    Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF (2008) Youden index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J 50:419–430PubMedPubMedCentralGoogle Scholar
  25. 25.
    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35PubMedPubMedCentralGoogle Scholar
  26. 26.
    Faraggi D (2000) The effect of random measurement error on receiver operating characteristic (ROC) curves. Stat Med 19:61–70PubMedGoogle Scholar
  27. 27.
    Reiser B (2000) Measuring the effectiveness of diagnostic markers in the presence of measurement error through the use of ROC curves. Stat Med 19:2115–2129PubMedGoogle Scholar
  28. 28.
    Hillis SL, Obuchowski NA, Schartz KM, Berbaum KS (2005) A comparison of the Dorfman-Berbaum-Metz and Obuchowski-Rockette methods for receiver operating characteristic (ROC) data. Stat Med 24:1579–1607PubMedGoogle Scholar
  29. 29.
    Hillis SL (2007) A comparison of denominator degrees of freedom methods formultiple observer ROC analysis. StatMed 26:596–619Google Scholar
  30. 30.
    Hillis SL, Berbaum KS, Metz CE (2008) Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Acad Radiol 15:647–661PubMedPubMedCentralGoogle Scholar
  31. 31.
    Pan X, Metz CE (1997) The “proper” binormal model: parametric receiver operating characteristic curve estimation with degenerate data. Acad Radiol 4:380–389PubMedGoogle Scholar
  32. 32.
    Ma H, Bandos AI, Rockette HE, Gur D (2013) On use of partial area under the ROC curve for evaluation of diagnostic performance. Stat Med 32:3449–3458PubMedPubMedCentralGoogle Scholar
  33. 33.
    Dodd LE, Pepe MS (2003) Partial AUC estimation and regression. Biometrics 59:614–623PubMedGoogle Scholar
  34. 34.
    Hillis SL, Metz CE (2012) An analytic expression for the binormal partial area under the ROC curve. Acad Radiol 19:1491–1498PubMedPubMedCentralGoogle Scholar
  35. 35.
    Barentsz JO, Richenberg J, Clements R et al (2012) ESUR prostate MR guidelines 2012. Eur Radiol 22:746–757PubMedPubMedCentralGoogle Scholar
  36. 36.
    Mehralivand S, Shih JH, Harmon S et al (2019) A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology 290:709–719PubMedGoogle Scholar
  37. 37.
    Rosenkrantz AB, Ginocchio LA, Cornfeld D et al (2016) Interobserver reproducibility of the PI-RADS version 2 lexicon: a multicenter study of six experienced prostate radiologists. Radiology 280:793–804PubMedPubMedCentralGoogle Scholar
  38. 38.
    Barrett T, Turkbey B, Choyke PL (2015) PI-RADS version 2: what you need to know. Clin Radiol 70:1165–1176PubMedPubMedCentralGoogle Scholar
  39. 39.
    Woo S, Kim SY, Cho JY, Kim SH (2017) Length of capsular contact on prostate MRI as a predictor of extracapsular extension: which is the most optimal sequence? Acta Radiol 58:489–497PubMedGoogle Scholar
  40. 40.
    Kamoi K, Okihara K, Hongo F et al (2017) MP93-20 tumor contact length with prostate capsule on magnetic resonance imaging as a potential predictor for biochemical recurrence after robotic-assisted radical prostatectomy. J Urol 197:e1244Google Scholar
  41. 41.
    Woo S, Cho JY, Kim SY, Kim SH (2015) Extracapsular extension in prostate cancer: added value of diffusion-weighted MRI in patients with equivocal findings on T2-weighted imaging. AJR Am J Roentgenol 204:W168–W175PubMedGoogle Scholar
  42. 42.
    Leeflang MM, Rutjes AW, Reitsma JB, Hooft L, Bossuyt PMa (2013) Variation of a test’s sensitivity and specificity with disease prevalence. CMAJ 185:E537–E544Google Scholar
  43. 43.
    Brenner H, Gefeller O (1997) Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence. Stat Med 16:981–991PubMedGoogle Scholar
  44. 44.
    Evans AJ, Henry PC, Van der Kwast TH et al (2008) Interobserver variability between expert urologic pathologists for extraprostatic extension and surgical margin status in radical prostatectomy specimens. Am J Surg Pathol 32:1503–1512PubMedGoogle Scholar
  45. 45.
    Ekici S, Ayhan A, Erkan I, Bakkaloğlu M, Ozen H (2003) The role of the pathologist in the evaluation of radical prostatectomy specimens. Scand J Urol Nephrol Suppl 37:387–391PubMedGoogle Scholar
  46. 46.
    van der Kwast TH, Collette L, Van Poppel H et al (2006) Impact of pathology review of stage and margin status of radical prostatectomy specimens (EORTC trial 22911). Virchows Arch 449:428–434Google Scholar
  47. 47.
    Danneman D, Wiklund F, Wiklund NP, Egevad L (2013) Prognostic significance of histopathological features of extraprostatic extension of prostate cancer. Histopathology 63:580–589PubMedGoogle Scholar
  48. 48.
    Maubon T, Branger N, Bastide C et al (2016) Impact of the extent of extraprostatic extension defined by Epstein’s method in patients with negative surgical margins and negative lymph node invasion. Prostate Cancer Prostatic Dis 19:317–321PubMedGoogle Scholar
  49. 49.
    Wheeler TM, Dillioglugil O, Kattan MW et al (1998) Clinical and pathological significance of the level and extent of capsular invasion in clinical stage T1-2 prostate cancer. Hum Pathol 29:856–862PubMedGoogle Scholar
  50. 50.
    McKenna DA, Coakley FV, Westphalen AC et al (2008) Prostate cancer: role of pretreatment MR in predicting outcome after external-beam radiation therapy–initial experience. Radiology 247:141–146PubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologySeoul National University Bundang HospitalSeongnam-siSouth Korea
  2. 2.Department of PathologySeoul National University Bundang HospitalSeongnam-siSouth Korea
  3. 3.Department of UrologySeoul National University Bundang HospitalSeongnam-siSouth Korea
  4. 4.Department of Prevention and Management, School of MedicineInha University HospitalIncheonSouth Korea

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