Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Clinical Research
  • Published:

Prostate MRI and clinicopathologic risk calculator to predict laterality of extraprostatic extension at radical prostatectomy

Prostate Cancer and Prostatic Diseases volume 28, pages 859–864 (2025)Cite this article

Subjects

Abstract

Background

Traditional nomograms can inform the presence of extraprostatic extension (EPE) but not laterality, which remains important for surgical planning, and have not fully incorporated multiparametric MRI data. We evaluated predictors of side-specific EPE on surgical pathology including MRI characteristics and developed side-specific EPE risk calculators.

Methods

This was a retrospective cohort of patients evaluated with mpMRI prior to radical prostatectomy (RP) in our eleven hospital healthcare system from July 2018-November 2022. The dominant side was defined pre-operatively using a tiered system based on laterality of highest biopsy Gleason Grade Group (GG), highest PIRADS lesion, number of lesions, and cancer volume. Univariable and multivariable logistic regression were performed for overall EPE, dominant side EPE, and non-dominant side EPE. Internal validation with leave one out and calibration curves were completed.

Results

EPE was identified in 53% (317/601) of patients at RP. Side-specific factors (PIRADS, GG, abutment) were only associated with EPE on their respective side. Final variables in the model associated with EPE on the dominant and non-dominant sides included age, log PSA density (PSAD), side-specific PIRADS 5, side-specific GG3–5, and percentage positivity of systematic cores. AUCs for dominant and non-dominant side EPE were 0.77 (95% CI 0.73—0.80) and 0.79 (95% CI 0.74–0.84), respectively. MRI-identified abutment and prostate health index (PHI) did not improve model discrimination. Risk calculators available online at https://rossnm1.shinyapps.io/PredictionOfEPELaterality/.

Conclusions

PSA, side-specific PIRADS, side-specific GG, and percentage positivity of systematic cores were associated with side-specific EPE at RP and incorporated into a risk calculator to assist in surgical planning and nerve-sparing decisions at time of RP.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: AUC Values for Overall EPE, Side Specific EPE, and MSKCC Nomograms.

Similar content being viewed by others

Data availability

Data are available for bona fide researchers who request it from the authors.

Code availability

Code can be provided by contacting the corresponding author by reasonable request.

References

  1. Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389:815–22.

    Article  PubMed  Google Scholar 

  2. Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378:1767–77.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Padhani AR, Petralia G, Sanguedolce F. Magnetic resonance imaging before prostate biopsy: time to talk. Eur Urol. 2016;69:1–3.

    Article  PubMed  Google Scholar 

  4. Siddiqui MR, Ansbro B, Shah PV, Aguiar JA, Li EV, Rich JM, et al. Real-world use of MRI for risk stratification prior to prostate biopsy. Prostate Cancer Prostatic Dis. 2023;26:353–9.

    Article  PubMed  Google Scholar 

  5. Jeong BC, Chalfin HJ, Lee SB, Feng Z, Epstein JI, Trock BJ, et al. The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol. 2015;67:342–6.

    Article  PubMed  Google Scholar 

  6. Tosoian JJ, Chappidi M, Feng Z, Humphreys EB, Han M, Pavlovich CP, et al. Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era. BJU Int. 2017;119:676–83.

    Article  CAS  PubMed  Google Scholar 

  7. Kattan MW, Eastham JA, Stapleton AM, Wheeler TM, Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst. 1998;90:766–71.

    Article  CAS  PubMed  Google Scholar 

  8. Cooperberg MR, Freedland SJ, Pasta DJ, Elkin EP, Presti JC Jr, Amling CL, et al. Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy. Cancer. 2006;107:2384–91.

    Article  PubMed  Google Scholar 

  9. de Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM. Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis. Eur Urol. 2016;70:233–45.

    Article  PubMed  Google Scholar 

  10. Soeterik TFW, van Melick HHE, Dijksman LM, Küsters-Vandevelde H, Stomps S, Schoots IG, et al. Development and external validation of a novel nomogram to predict side-specific extraprostatic extension in patients with prostate cancer undergoing radical prostatectomy. Eur Urol Oncol. 2022;5:328–37.

    Article  PubMed  Google Scholar 

  11. Martini A, Gupta A, Lewis SC. Development and internal validation of a side-specific, multparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int. 2018;122:1025–33.

    Article  CAS  PubMed  Google Scholar 

  12. Veerman H, Heymans MW, van der Poel HG. External validation of a prediction model for side-specific extraprostatic extension of prostate cancer at robot-assisted radical prostatectomy. Eur Urol Open Sci. 2022;37:50–2.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Godtman RA, Kollberg KS, Pihl CG, Månsson M, Hugosson J. The association between age, prostate cancer risk, and higher gleason score in a long-term screening program: results from the göteborg-1 prostate cancer screening trial. Eur Urol. 2022;82:311–7.

    Article  PubMed  Google Scholar 

  14. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Eur Urol. 2015;67:1142–51.

    Article  PubMed  Google Scholar 

  15. Diamand R, Ploussard G, Roumiguié M, Oderda M, Benamran D, Fiard G, et al. External validation of a multiparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension and seminal vesicle invasion in prostate cancer patients undergoing radical prostatectomy. Eur Urol. 2021;79:180–5.

    Article  PubMed  Google Scholar 

  16. Stephenson AJ, Scardino PT, Eastham JA, Bianco FJ Jr, Dotan ZA, Fearn PA, et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst. 2006;98:715–7.

    Article  PubMed  Google Scholar 

  17. Freifeld Y, Diaz de Leon A, Xi Y. Diagnostic performance of prospectively assigned likert scale scores to determine extraprostatic extension and seminal vesicle invasion with multiparametric MRI of the prostate. AJR Am J Roentgenol. 2019;212:576–81.

    Article  PubMed  Google Scholar 

  18. Reisæter LA, Halvorsen OJ, Beisland C. Assessing extraprostatic extension with multiparametric MRI of the prostate: mehralivand extraprostatic extension grade or extraprostatic extension likert scale? Radio Imaging Cancer. 2020;2:e190071.

    Article  Google Scholar 

  19. Mehralivand S, Shih JH, Harmon S. A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology. 2019;290:709–19.

    Article  PubMed  Google Scholar 

  20. Valentin B, Schimmoller L, Ullrich T. Magnetic resonance imaging improves the prediction of tumor stage in localized prostate cancer. Abdom Radio. 2021;46:2751–9.

    Article  CAS  Google Scholar 

  21. Bakir B, Onay A, Vural M. Can extraprostatic extension be predicted by tumor-capsule contact length in prostate cancer? relationship with international society of urological pathology grade groups. AJR Am J Roentgenol. 2020;214:588–96.

    Article  PubMed  Google Scholar 

  22. van den Berg I, Soeterik TFW, van der Hoeven E, Claassen B, Brink WM, Baas DJH, et al. The development and external validation of artificial intelligence-driven MRI-based models to improve prediction of lesion-specific extraprostatic extension in patients with prostate cancer. Cancers (Basel). 2023;15:5452.

  23. Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, et al. Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the society of abdominal radiology prostate cancer disease-focused panel. Radiology. 2020;296:76–84.

    Article  PubMed  Google Scholar 

  24. Moroianu SL, Bhattacharya I, Seetharaman A. Computation detection of extraprostatic extension of prostate cancer on multiparametric MRI using deep learning. Cancers (Basel). 2022;14:2821.

    Article  PubMed  Google Scholar 

Download references

Funding

HDP is supported by the Prostate Cancer Foundation Young Investigator Award.

Author information

Author notes

  1. These authors contributed equally: Ashley E. Ross, Hiten D. Patel.

Authors and Affiliations

  1. Northwestern University, Feinberg School of Medicine, Department of Urology, Chicago, IL, 60611, USA

    Eric V. Li, Jonathan A. Aguiar, Mohammad R. Siddiqui, Clayton Neill, Edward M. Schaeffer, Ashley E. Ross & Hiten D. Patel

  2. Northwestern University, Feinberg School of Medicine, Department of Preventive Medicine-Division of Biostatistics, Chicago, IL, 60611, USA

    Sai Kumar & Zequn Sun

Authors
  1. Eric V. Li
    View author publications

    Search author on:PubMed Google Scholar

  2. Sai Kumar
    View author publications

    Search author on:PubMed Google Scholar

  3. Jonathan A. Aguiar
    View author publications

    Search author on:PubMed Google Scholar

  4. Mohammad R. Siddiqui
    View author publications

    Search author on:PubMed Google Scholar

  5. Zequn Sun
    View author publications

    Search author on:PubMed Google Scholar

  6. Clayton Neill
    View author publications

    Search author on:PubMed Google Scholar

  7. Edward M. Schaeffer
    View author publications

    Search author on:PubMed Google Scholar

  8. Ashley E. Ross
    View author publications

    Search author on:PubMed Google Scholar

  9. Hiten D. Patel
    View author publications

    Search author on:PubMed Google Scholar

Contributions

EVL contributed with conceptualization, methodology, formal analysis, investigation, data curation, writing of original draft and editing, and visualization. SK assisted with methodology, formal analysis, investigation, resources, visualization, and reviewing and editing of manuscript. JAA assisted with investigation, writing of original draft, and review and editing of the manuscript. MRS assisted with data curation and review and editing of manuscript. ZS contributed to investigation and review of manuscript. CN contributed to resources, data acquisition, and review and editing of manuscript. EMS provided conceptualization, review and editing of manuscript and supervision. AER contributed with conceptualization, review and editing of manuscript, supervision, project administration, and funding acquisition. HDP contributed with conceptualization, review and editing of manuscript, supervision, project administration, and funding acquisition.

Corresponding author

Correspondence to Eric V. Li.

Ethics declarations

Competing interests

AER is engaged in consulting with American Society of Clinical Oncology (ASCO), Astellas, Bayer HealthCare Pharmaceuticals, Blue Earth Diagnostics, Janssen Biotech, Lantheus Medical Imaging, Myovant Sciences, NCCN, Pfizer, Tempus Health, and Veracyte. EMS is engaged in consulting with Astellas, Lantheus Medical Imaging, Pfizer. The other contributing authors have no conflicts of interest to declare.

Ethics approval and consent to participate

The study was reviewed under IRB STU00214996 issued by Northwestern University. The study was performed in accordance with the Declaration of Helsinki.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, E.V., Kumar, S., Aguiar, J.A. et al. Prostate MRI and clinicopathologic risk calculator to predict laterality of extraprostatic extension at radical prostatectomy. Prostate Cancer Prostatic Dis 28, 859–864 (2025). https://doi.org/10.1038/s41391-024-00928-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41391-024-00928-7

This article is cited by

Search

Advanced search

Quick links