Skip to main content

Advertisement

SpringerLink
Log in

External validation of Cormio nomogram for predicting all prostate cancers and clinically significant prostate cancers

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

Recently, the Cormio et al. nomogram has been developed to predict prostate cancer (PCa) and clinically significant PCa using benign prostatic obstruction parameters. The aim of the present study was to externally validate the nomogram in a multicentric cohort.

Methods

Between 2013 and 2019, patients scheduled for ultrasound-guided prostate biopsy were prospectively enrolled at 11 Italian institutions. Demographic, clinical and histological data were collected and analysed. Discrimination and calibration of Cormio nomogram were assessed with the receiver operator characteristics (ROC) curve and calibration plots. The clinical net benefit of the nomogram was assessed with decision curve analysis. Clinically significant PCa was defined as ISUP grade group > 1.

Results

After accounting for inclusion criteria, 1377 patients were analysed. 816/1377 (59%) had cancer at final pathology (574/816, 70%, clinically significant PCa). Multivariable analysis showed age, prostate volume, DRE and post-voided residual volume as independent predictors of any PCa. Discrimination of the nomogram for cancer was 0.70 on ROC analysis. Calibration of the nomogram was excellent (p = 0.94) and the nomogram presented a net benefit in the 40–80% range of probabilities. Multivariable analysis for predictors of clinically significant PCa found age, PSA, prostate volume and DRE as independent variables. Discrimination of the nomogram was 0.73. Calibration was poor (p = 0.001) and the nomogram presented a net benefit in the 25–75% range of probabilities.

Conclusion

We confirmed that the Cormio nomogram can be used to predict the risk of PCa in patients at increased risk. Implementation of the nomogram in clinical practice will better define its role in the patient’s counselling before prostate biopsy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. EAU guidelines: prostate cancer (2019). https://uroweb.org/guideline/prostate-cancer/#5. Accessed 17 Aug 2019

  2. Serag H, Banerjee S, Saeb-Parsy K, Irving S, Wright K, Stearn S et al (2012) Risk profiles of prostate cancers identified from UK primary care using national referral guidelines. Br J Cancer 106:436–439. https://doi.org/10.1038/bjc.2011.596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bokhorst LP, Zhu X, Bul M, Bangma CH, Schröder FH, Roobol MJ (2012) Positive predictive value of prostate biopsy indicated by prostate-specific-antigen-based prostate cancer screening: trends over time in a European randomized trial. BJU Int 110:1654–1660. https://doi.org/10.1111/j.1464-410X.2012.11481.x

    Article  CAS  PubMed  Google Scholar 

  4. Louie KS, Seigneurin A, Cathcart P, Sasieni P (2015) Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis. Ann Oncol 26:848–864. https://doi.org/10.1093/annonc/mdu525

    Article  CAS  PubMed  Google Scholar 

  5. Vedder MM, Bekker-Grob EW, Lilja HG, Vickers AJ, Leenders GJ, Steyerberg EW, Roobol MJ (2014) The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men. Eur Urol 66(6):1109–1115

    Article  CAS  Google Scholar 

  6. Leyten GH, Hessels D, Jannink SA et al (2014) Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol 65(3):534–542

    Article  CAS  Google Scholar 

  7. Boegemann M, Stephan C, Cammann H, Vincendeau S, Houlgatte A, Jung K, Blanchet JS, Semjonow A (2016) The percentage of prostate-specific antigen (PSA) isoform [-2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged %3c/=65 years. BJU Int 117(1):72–79. https://doi.org/10.1111/bju.13139(Epub 2015 May 24)

    Article  CAS  PubMed  Google Scholar 

  8. Bryant RJ, Sjoberg DD, Vickers AJ, Robinson MC, Kumar R, Marsden L, Davis M, Scardino PT, Donovan J, Neal DE, Lilja H, Hamdy FC (2015) Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djv095

    Article  PubMed  PubMed Central  Google Scholar 

  9. Falagario UG, Martini A, Wajswol E et al (2019) Avoiding unnecessary magnetic resonance imaging (MRI) and biopsies: negative and positive predictive value of MRI according to prostate-specific antigen density, 4Kscore and risk calculators. Eur Urol Oncol. https://doi.org/10.1016/j.euo.2019.08.015(Epub ahead of print)

    Article  PubMed  Google Scholar 

  10. Cormio L, Cindolo L, Troiano F et al (2018) Development and internal validation of novel nomograms based on benign prostatic obstruction-related parameters to predict the risk of prostate cancer at first prostate biopsy. Front Oncol 8:438. https://doi.org/10.3389/fonc.2018.00438(eCollection 2018)

    Article  PubMed  PubMed Central  Google Scholar 

  11. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA et al (2016) The 2014 international society of urological pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol 40:244–252. https://doi.org/10.1097/PAS.0000000000000530

    Article  PubMed  Google Scholar 

  12. Antonelli A, Fugini AV, Tardanico R, Giovanessi L, Zambolin T, Simeone C (2014) The percentage of core involved by cancer is the best predictor of insignificant prostate cancer, according to an updated definition (tumor volume up to 2.5 cm3): analysis of a cohort of 210 consecutive patients with low-risk disease. Urology 83(1):28–32

    Article  Google Scholar 

  13. Autorino R, De Sio M, Di Lorenzo G et al (2005) How to decrease pain during transrectal ultrasound guided prostate biopsy: a look at the literature. J Urol 174(6):2091–2097

    Article  Google Scholar 

  14. Azevedo N, Verbeek JFM, Nieboer D, Bangma CH, Roobol MJ (2018) Head-to-head comparison of prostate cáncer risk calculators predicting biopsy outcome. Transl Androl Urol 7(1):18–26. https://doi.org/10.21037/tau.2017.12.21

    Article  PubMed  PubMed Central  Google Scholar 

  15. Schoots IG, Padhani AR (2019) Personalizing prostate cancer diagnosis with multivariate risk prediction tools: how should prostate MRI be incorporated? World J Urol. https://doi.org/10.1007/s00345-019-02899-0(Epub ahead of print)

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cormio L, Lucarelli G, Selvaggio O et al (2016) Absence of bladder outlet obstruction is an independent risk factor for prostate cancer in men undergoing prostate biopsy. Medicine (Baltimore) 95(7):e2551

    Article  Google Scholar 

  17. Cormio L, Lucarelli G, Netti GS et al (2015) Post-void residual urinary volume is an independent predictor of biopsy results in men at risk for prostate cancer. Anticancer Res 35(4):2175–2182

    PubMed  Google Scholar 

  18. Cicione A, Cormio L, Cantiello F et al (2017) Presence and severity of lower urinary tract symptoms are inversely correlated with the risk of prostate cancer on prostate biopsy. Minerva Urol Nefrol 69(5):486–492

    PubMed  Google Scholar 

  19. Kranse R, Roobol M, Schroder FH (2008) A graphical device to represent the outcomes of a logistic regression analysis. Prostate 68:1674–1680

    Article  Google Scholar 

  20. Dong F, Kattan MW, Steyerberg EW et al (2008) Validation of pretreatment nomograms for predicting indolent prostate cancer: efficacy in contemporary urological practice. J Urol 180(1):150–154. https://doi.org/10.1016/j.juro.2008.03.053(discussion 154, Epub 2008 May 15)

    Article  PubMed  Google Scholar 

  21. Roobol MJ, Schröder FH, Hugosson J et al (2012) Importance of prostate volume in the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators: results from the prostate biopsy collaborative group. World J Urol 30(2):149–155. https://doi.org/10.1007/s00345-011-0804-y(Epub 2011 Dec 28)

    Article  PubMed  Google Scholar 

  22. Rove KO, Crawford ED (2012) Randomized controlled screening trials for prostate cancer using prostate-specific antigen: a tale of contrasts. World J Urol 30(2):137–142. https://doi.org/10.1007/s00345-011-0799-4(Epub 2011 Nov 25)

    Article  PubMed  Google Scholar 

  23. Moreira DM, Freitas ODM, Nickel JC, Andriole GL, Castro-Santamaria R, Freedland SJ (2017) The combination of histological prostate atrophy and inflammation is associated with lower risk of prostate cancer in biopsy specimens. Prostate Cancer Prostatic Dis 20:413–417. https://doi.org/10.1038/pcan.2017.30

    Article  CAS  PubMed  Google Scholar 

  24. De Nunzio C, Kramer G, Marberger M, Montironi R, Nelson W, Schröder F et al (2011) The controversial relationship between benign prostatic hyperplasia and prostate cancer: the role of inflammation. Eur Urol 60:106–117. https://doi.org/10.1016/j.eururo.2011.03.055

    Article  CAS  PubMed  Google Scholar 

  25. Falagario U, Selvaggio O, Carrieri G et al (2018) Prostatic inflammation is associated with benign prostatic hyperplasia rather than prostate cancer. J Gerontol Geriatr 2018(4):178–182

    Google Scholar 

  26. Drost FH, Osses DF, Nieboer D et al (2019) Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev 4:CD012663. https://doi.org/10.1002/14651858.CD012663.pub2

    Article  PubMed  Google Scholar 

  27. van der Leest M, Cornel E, Israël B et al (2019) Head-to-head comparison of transrectal ultrasound-guided prostate biopsy versus multiparametric prostate resonance imaging with subsequent magnetic resonance-guided biopsy in biopsy-naïve men with elevated prostate-specific antigen: a large prospective multicenter clinical study. Eur Urol 75(4):570–578. https://doi.org/10.1016/j.eururo.2018.11.023(Epub 2018 Nov 23)

    Article  PubMed  Google Scholar 

  28. Padhani AR, Barentsz J, Villeirs G et al (2019) PI-RADS Steering Committee: the PI-RADS multiparametric MRI and MRI-directed biopsy pathway. Radiology 292(2):464–474. https://doi.org/10.1148/radiol.2019182946 (Epub 2019 Jun 11)

    Article  PubMed  PubMed Central  Google Scholar 

  29. Thompson IM, Ankerst DP, Chi C et al (2006) Assessing prostate cancer risk: results from the prostate cancer prevention trial. J Natl Cancer Inst 98:529–534

    Article  Google Scholar 

Download references

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Urology Department, “Villa Stuart” Private Hospital, Via Trionfale, 5952-00136, Rome, Italy

    Luca Cindolo

  2. Urology Department, “San Carlo di Nancy” Hospital, Rome, Italy

    Riccardo Bertolo, Pierluigi Bove & Matteo Vittori

  3. Department of Urology, Azienda Ospedaliera Careggi, Universitá di Firenze, Florence, Italy

    Andrea Minervini, Francesco Sessa & Gianluca Muto

  4. UOC Urologia ASST Valle Olona, Busto Arsizio, Italy

    Giorgio Bozzini

  5. Department of Urology, SS. Annunziata Hospital, Chieti, Italy

    Pietro Castellan & Luigi Schips

  6. U.O.C. Urologia Ospedale Vittorio Emanuele, Catania, Italy

    Filippo Mugavero & Mario Falsaperla

  7. Department of Urology, San Bassiano Hospital, Bassano del Grappa, Italy

    Antonio Celia & Maida Bada

  8. Department of Robotic Urological Surgery, Abano Terme Hospital, Abano Terme, Italy

    Angelo Porreca & Marco Giampaoli

  9. Urology Unit, Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy

    Antonio Pastore & Yazan Al Salhi

  10. Urologic Clinic, University Hospital, Ospedale Policlinico, Azienda Ospedaliera Universitaria Integrata, Verona, Italy

    Giovanni Novella, Riccardo Rizzetto, Nicoló Trabacchin & Alessandro Antonelli

  11. Urologia Ospedale San Raffaele Turro, Milano, Italy

    Guglielmo Mantica & Giovannalberto Pini

  12. Department of Urology, Ospedale Sant’Andrea-Universitá di Roma “Sapienza”, Rome, Italy

    Riccardo Lombardo, Andrea Tubaro & Cosimo De Nunzio

Authors
  1. Luca Cindolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riccardo Bertolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Minervini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesco Sessa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gianluca Muto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pierluigi Bove
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matteo Vittori
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giorgio Bozzini
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pietro Castellan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Filippo Mugavero
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mario Falsaperla
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Luigi Schips
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Antonio Celia
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Maida Bada
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Angelo Porreca
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Antonio Pastore
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yazan Al Salhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Marco Giampaoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Giovanni Novella
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Riccardo Rizzetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Nicoló Trabacchin
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Guglielmo Mantica
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Giovannalberto Pini
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Riccardo Lombardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Andrea Tubaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Alessandro Antonelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Cosimo De Nunzio
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Protocol/project development: LC. Data collection or management: AM, FS, GM, PB, MV, GB, PC, FM, MF, LS, AC, MB, AP, AP, YAS, MG, GN, RR, NT, GM, GP. Data analysis: CDN, RL. Manuscript writing/editing: RB, LC. Manuscript overview: AT, AA.

Corresponding author

Correspondence to Luca Cindolo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants

The study was approved by the institutional research ethics committee and performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (JPG 185 kb)

Supplementary file2 (DOCX 14 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cindolo, L., Bertolo, R., Minervini, A. et al. External validation of Cormio nomogram for predicting all prostate cancers and clinically significant prostate cancers. World J Urol 38, 2555–2561 (2020). https://doi.org/10.1007/s00345-019-03058-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-019-03058-1

Keywords

Search

Navigation