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World Journal of Urology

, Volume 36, Issue 4, pp 629–637 | Cite as

A prospective study evaluating indirect MRI-signs for the prediction of extraprostatic disease in patients with prostate cancer: tumor volume, tumor contact length and tumor apparent diffusion coefficient

  • Erik Rud
  • Lien Diep
  • Eduard Baco
Original Article
  • 147 Downloads

Abstract

Objective

The aim of this study was to evaluate three indirect MRI signs for predicting extraprostatic disease in patients referred to radical prostatectomy: index tumor volume (MTV), apparent diffusion coefficient (ADC) and tumor contact length (TCL).

Materials and methods

This prospective study included 183 patients with biopsy proven prostate cancer. In all patients the MTV (ml), ADC (× 10−5 mm2/s) and TCL (mm) of the index tumor were registered at the preoperative MRI. Whole-mounted microscopical examination classified each patient as having either localized- or extraprostatic disease. The Youden index was used to identify the optimal cut-off values for predicting extraprostatic disease. Univariate regression analyses were conducted to estimate the odds ratio (OR) with 95% confidence intervals (CI). Results were stratified upon zonal location of the index tumor.

Results

Extraprostatic disease was identified in 103 (56%) patients. The risk of extraprostatic disease was nine times higher in peripheral zone tumors with ADC ≤ 89 (OR 9.1, 95% CI 4.2–19.6), five times higher in MTV ≥ 0.9 ml (OR 5.5, 95% CI 2.6–11.4) and five times higher in case of TCL ≥ 14 mm (OR 4.9, 95% CI 2.3–10.2). None of the indirect MRI signs could predict extraprostatic disease for transition zone tumors.

Conclusion

The MTV, ADC and TCL are all significant predictors of extraprostatic disease for peripheral zone tumors, while none of the indirect signs were useful for transition zone tumors.

Keywords

Prostate cancer MRI Staging Extraprostatic disease Indirect signs Tumor capsule length Tumor contact length ADC Tumor volume 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. 1.
    Rud E, Klotz D, Rennesund K et al (2014) Preoperative magnetic resonance imaging for detecting uni- and bilateral extraprostatic disease in patients with prostate cancer. World J Urol.  https://doi.org/10.1007/s00345-014-1362-x PubMedCentralGoogle Scholar
  2. 2.
    Obek C, Louis P, Civantos F, Soloway MS (1999) Comparison of digital rectal examination and biopsy results with the radical prostatectomy specimen. JURO 161:494–498 (discussion 498–9) Google Scholar
  3. 3.
    de Rooij M, Hamoen EHJ, Witjes JA et al (2015) Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis. Eur Urol.  https://doi.org/10.1016/j.eururo.2015.07.029 Google Scholar
  4. 4.
    Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS prostate imaging—reporting and data system: 2015, version 2. Eur Urol 69:16–40.  https://doi.org/10.1016/j.eururo.2015.08.052 CrossRefPubMedGoogle Scholar
  5. 5.
    Yu KK, Hricak H, Alagappan R et al (1997) Detection of extracapsular extension of prostate carcinoma with endorectal and phased-array coil MR imaging: multivariate feature analysis. Radiology 202:697–702.  https://doi.org/10.1148/radiology.202.3.9051019 CrossRefPubMedGoogle Scholar
  6. 6.
    Outwater EK, Petersen RO, Siegelman ES et al (1994) Prostate carcinoma: assessment of diagnostic criteria for capsular penetration on endorectal coil MR images. Radiology 193:333–339.  https://doi.org/10.1148/radiology.193.2.7972739 CrossRefPubMedGoogle Scholar
  7. 7.
    Kim KH, Lim SK, Shin T-Y et al (2013) Tumor volume adds prognostic value in patients with organ-confined prostate cancer. Ann Surg Oncol 20:3133–3139.  https://doi.org/10.1245/s10434-013-3016-4 CrossRefPubMedGoogle Scholar
  8. 8.
    Rud E, Klotz D, Rennesund K et al (2014) Detection of the index tumor and tumor volume in prostate cancer using T2w and DW MRI alone. BJU Int.  https://doi.org/10.1111/bju.12637 Google Scholar
  9. 9.
    Baco E, Rud E, Vlatkovic L et al (2014) Predictive value of magnetic resonance imaging determined tumor contact length for extra-capsular extension of prostate cancer. J Urol.  https://doi.org/10.1016/j.juro.2014.08.084 PubMedGoogle Scholar
  10. 10.
    Rosenkrantz AB, Shanbhogue AK, Wang A et al (2016) Length of capsular contact for diagnosing extraprostatic extension on prostate MRI: assessment at an optimal threshold. J Magn Reson Imaging 43:990–997.  https://doi.org/10.1002/jmri.25040 CrossRefPubMedGoogle Scholar
  11. 11.
    Bratan F, Melodelima C, Souchon R et al (2014) How accurate is multiparametric MR imaging in evaluation of prostate cancer volume? Radiology.  https://doi.org/10.1148/radiol.14140524 Google Scholar
  12. 12.
    Hambrock T, Somford DM, Huisman HJ et al (2011) Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer. Radiology 259:453–461.  https://doi.org/10.1148/radiol.11091409 CrossRefPubMedGoogle Scholar
  13. 13.
    Itou Y, Nakanishi K, Narumi Y et al (2011) Clinical utility of apparent diffusion coefficient (ADC) values in patients with prostate cancer: can ADC values contribute to assess the aggressiveness of prostate cancer? J Magn Reson Imaging 33:167–172.  https://doi.org/10.1002/jmri.22317 CrossRefPubMedGoogle Scholar
  14. 14.
    Rud E, Baco E, Eggesbø HB (2012) MRI and ultrasound-guided prostate biopsy using soft image fusion. Anticancer Res 32:3383–3389PubMedGoogle Scholar
  15. 15.
    Magi-Galluzzi C, Evans AJ, Delahunt B et al (2010) 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 24:26–38.  https://doi.org/10.1038/modpathol.2010.158 CrossRefPubMedGoogle Scholar
  16. 16.
    Lim C, Flood TA, Hakim SW et al (2016) Evaluation of apparent diffusion coefficient and MR volumetry as independent associative factors for extra-prostatic extension (EPE) in prostatic carcinoma. J Magn Reson Imaging 43:726–736.  https://doi.org/10.1002/jmri.25033 CrossRefPubMedGoogle Scholar
  17. 17.
    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.e8.  https://doi.org/10.1016/j.urolonc.2016.07.013 CrossRefGoogle Scholar
  18. 18.
    Granja MF, Pedraza CM, Flórez DC et al (2017) Predicting extracapsular involvement in prostate cancer through the tumor contact length and the apparent diffusion coefficient. Radiologia 59:313–320.  https://doi.org/10.1016/j.rx.2017.03.003 CrossRefPubMedGoogle Scholar
  19. 19.
    Ranstam J (2008) Analysis units. Acta Radiol 49:371–372.  https://doi.org/10.1080/02841850801977312 CrossRefPubMedGoogle Scholar
  20. 20.
    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. Am J Roentgenol 204:W168–W175.  https://doi.org/10.2214/AJR.14.12939 CrossRefGoogle Scholar
  21. 21.
    Kim CK, Park SY, Park JJ, Park BK (2014) Diffusion-weighted MRI as a predictor of extracapsular extension in prostate cancer. Am J Roentgenol 202:W270–W276.  https://doi.org/10.2214/AJR.13.11333 CrossRefGoogle Scholar
  22. 22.
    Rosenkrantz AB, Chandarana H, Gilet A et al (2013) Prostate cancer: utility of diffusion-weighted imaging as a marker of side-specific risk of extracapsular extension. J Magn Reson Imaging 38:312–319.  https://doi.org/10.1002/jmri.23972 CrossRefPubMedGoogle Scholar
  23. 23.
    Lawrence EM, Gallagher FA, Barrett T et al (2014) Preoperative 3-T diffusion-weighted MRI for the qualitative and quantitative assessment of extracapsular extension in patients with intermediate- or high-risk prostate cancer. Am J Roentgenol 203:W280–W286.  https://doi.org/10.2214/AJR.13.11754 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Radiology and Nuclear MedicineOslo University Hospital, AkerOsloNorway
  2. 2.Oslo Centre for Biostatistics and EpidemiologyOslo University HospitalOsloNorway
  3. 3.Division of Surgery, Inflammatory Diseases and Transplantation, Department of urologyOslo University HospitalOsloNorway

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