Skip to main content

Advertisement

SpringerLink
Log in

A systematic review of the tools available for predicting survival and managing patients with urothelial carcinomas of the bladder and of the upper tract in a curative setting

  • Topic paper
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Objectives

To identify the predictive tools which have emerged recently in the field of urothelial carcinomas.

Materials and methods

We performed a thorough MEDLINE literature review using a combination of the following keywords: urothelial carcinoma, transitional cell carcinoma, bladder, renal pelvis, ureter, predictive tools, predictive models and nomograms. We found 117 articles, but only the relevant reports were selected.

Results

The majority of available tools are prediction models, particularly nomograms. These models combine good performance accuracy with ease of use. They appear to be more accurate than risk grouping or tree modeling and are more suitable for clinicians than artificial intelligence. The most recent nomograms have been designed to be used in daily clinical practice and are even available as computer or smartphone applications. They focus on pathological outcomes or more frequently on survival statistics or recurrence risk after surgery. They provide an accurate prediction of disease evolution and may help clinicians to choose the most appropriate treatment option. However, these prediction tools still need to be validated and regularly utilized.

Conclusion

Predictive tools represent very helpful clinical decision-making aids but need to be validated in larger populations.

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

Similar content being viewed by others

References

  1. Siegel R, Naishadham D, Jemal A (2012) Cancer statistics 2012. CA Cancer J Clin 62:10–29

    Article  PubMed  Google Scholar 

  2. Catto JW, Yates DR, Rehman I, Azzouzi AR, Patterson J, Sibony M et al (2007) Behavior of urothelial carcinoma with respect to anatomical location. J Urol 177:1715–1720

    Article  PubMed  CAS  Google Scholar 

  3. Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics 2010. CA Cancer J Clin 60:277–300

    Article  PubMed  Google Scholar 

  4. Nguyen CT, Stephenson AJ, Kattan MW (2010) Are nomograms needed in the management of bladder cancer? Urol Oncol 28:102–107

    Article  PubMed  Google Scholar 

  5. Lughezzani G, Briganti A, Karakiewicz PI, Kattan MW, Montorsi F, Shariat SF et al (2010) Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature. Eur Urol 58:687–700

    Article  PubMed  Google Scholar 

  6. Vickers AJ (2011) Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol 29:2951–2952

    Article  PubMed  Google Scholar 

  7. Vickers AJ, Cronin AM (2010) Everything you always wanted to know about evaluating prediction models (but were too afraid to ask). Urology 76:1298–1301

    Article  PubMed  Google Scholar 

  8. Shariat SF, Capitanio U, Jeldres C, Karakiewicz PI (2009) Can nomograms be superior to other prediction tools? BJU Int 103:492–495; discussion 495–497

    Google Scholar 

  9. D’Amico AV, Whittington R, Malkowicz SB, Schultz D, Blank K, Broderick GA et al (1998) Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA 280:969–974

    Article  PubMed  Google Scholar 

  10. Kattan MW (2003) Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 170:S6–S10

    Article  PubMed  Google Scholar 

  11. Catto JW, Linkens DA, Abbod MF, Chen M, Burton JL, Feeley KM et al (2003) Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks. Clin Cancer Res 9:4172–4177

    PubMed  Google Scholar 

  12. Shariat SF, Karakiewicz PI, Godoy G, Lerner SP (2009) Use of nomograms for predictions of outcome in patients with advanced bladder cancer. Ther Adv Urol 1:13–26

    Article  PubMed  Google Scholar 

  13. Kattan MW, Vickers AJ, Yu C, Bianco FJ, Cronin AM, Eastham JA et al (2009) Preoperative and postoperative nomograms incorporating surgeon experience for clinically localized prostate cancer. Cancer 115:1005–1010

    Article  PubMed  Google Scholar 

  14. Catto JW, Abbod MF, Linkens DA, Larre S, Rosario DJ, Hamdy FC (2009) Neurofuzzy modeling to determine recurrence risk following radical cystectomy for nonmetastatic urothelial carcinoma of the bladder. Clin Cancer Res 15:3150–3155

    Article  PubMed  Google Scholar 

  15. Fernandez-Gomez J, Madero R, Solsona E, Unda M, Martinez-Pineiro L, Ojea A et al (2011) The EORTC tables overestimate the risk of recurrence and progression in patients with non-muscle-invasive bladder cancer treated with bacillus Calmette-Guerin: external validation of the EORTC risk tables. Eur Urol 60:423–430

    Article  PubMed  Google Scholar 

  16. Karakiewicz PI, Shariat SF, Palapattu GS, Perrotte P, Lotan Y, Rogers CG et al (2006) Precystectomy nomogram for prediction of advanced bladder cancer stage. Eur Urol 50:1254–1262

    Article  PubMed  Google Scholar 

  17. Taylor JM, Feifer A, Savage CJ, Maschino AC, Bernstein M, Herr HW et al (2012) Evaluating the utility of a preoperative nomogram for predicting 90-day mortality following radical cystectomy for bladder cancer. BJU Int 109:855–859

    Article  PubMed  Google Scholar 

  18. Vickers AJ, Cronin AM, Kattan MW, Gonen M, Scardino PT, Milowsky MI et al (2009) Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach. Cancer 115:5460–5469

    Article  PubMed  Google Scholar 

  19. El-Mekresh M, Akl A, Mosbah A, Abdel-Latif M, Abol-Enein H, Ghoneim MA (2009) Prediction of survival after radical cystectomy for invasive bladder carcinoma: risk group stratification, nomograms or artificial neural networks? J Urol 182:466–472

    Article  PubMed  Google Scholar 

  20. Bassi P, Sacco E, De Marco V, Aragona M, Volpe A (2007) Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. BJU Int 99:1007–1012

    Article  PubMed  Google Scholar 

  21. Bochner BH, Kattan MW, Vora KC (2006) Postoperative nomogram predicting risk of recurrence after radical cystectomy for bladder cancer. J Clin Oncol 24:3967–3972

    Article  PubMed  Google Scholar 

  22. Shariat SF, Karakiewicz PI, Palapattu GS, Amiel GE, Lotan Y, Rogers CG et al (2006) Nomograms provide improved accuracy for predicting survival after radical cystectomy. Clin Cancer Res 12:6663–6676

    Article  PubMed  Google Scholar 

  23. Shariat SF, Chromecki TF, Cha EK, Karakiewicz PI, Sun M, Fradet Y et al (2012) Risk stratification of organ confined bladder cancer after radical cystectomy using cell cycle related biomarkers. J Urol 187:457–462

    Article  PubMed  Google Scholar 

  24. Bassi PF, Bongiovanni L, Racioppi M, Volpe A, D’Agostino D, Gardi M (2011) Postoperative nomogram for invasive bladder cancer: does it really work? A multicenter cohort study. Urol Oncol 29:698–702

    Article  PubMed  Google Scholar 

  25. Nuhn P, May M, Sun M, Fritsche HM, Brookman-May S, Buchner A et al (2012) External validation of postoperative nomograms for prediction of all-cause mortality, cancer-specific mortality, and recurrence in patients with urothelial carcinoma of the bladder. Eur Urol 61:58–64

    Article  PubMed  Google Scholar 

  26. Zaak D, Burger M, Otto W, Bastian PJ, Denzinger S, Stief CG et al (2010) Predicting individual outcomes after radical cystectomy: an external validation of current nomograms. BJU Int 106:342–348

    Article  PubMed  Google Scholar 

  27. Shariat SF, Karakiewicz PI, Ashfaq R, Lerner SP, Palapattu GS, Cote RJ et al (2008) Multiple biomarkers improve prediction of bladder cancer recurrence and mortality in patients undergoing cystectomy. Cancer 112:315–325

    Article  PubMed  Google Scholar 

  28. Gakis G, Todenhofer T, Renninger M, Schilling D, Sievert KD, Schwentner C et al (2011) Development of a new outcome prediction model in carcinoma invading the bladder based on preoperative serum C-reactive protein and standard pathological risk factors: the TNR-C score. BJU Int 108:1800–1805

    Article  PubMed  CAS  Google Scholar 

  29. Riester M, Taylor JM, Feifer A, Koppie T, Rosenberg JE, Downey RJ et al (2012) Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer. Clin Cancer Res 18:1323–1333

    Article  PubMed  CAS  Google Scholar 

  30. Margulis V, Lotan Y, Montorsi F, Shariat SF (2008) Predicting survival after radical cystectomy for bladder cancer. BJU Int 102:15–22

    Article  PubMed  Google Scholar 

  31. Favaretto RL, Shariat SF, Savage C, Godoy G, Chade DC, Kaag M et al (2012) Combining imaging and ureteroscopy variables in a preoperative multivariable model for prediction of muscle-invasive and non-organ confined disease in patients with upper tract urothelial carcinoma. BJU Int 109:77–82

    Article  PubMed  Google Scholar 

  32. Jeldres C, Sun M, Lughezzani G, Isbarn H, Shariat SF, Widmer H et al (2010) Highly predictive survival nomogram after upper urinary tract urothelial carcinoma. Cancer 116:3774–3784

    Article  PubMed  Google Scholar 

  33. Yates DR, Hupertan V, Colin P, Ouzzane A, Descazeaud A, Long JA et al (2012) Cancer-specific survival after radical nephroureterectomy for upper urinary tract urothelial carcinoma: proposal and multi-institutional validation of a post-operative nomogram. Br J Cancer 106:1083–1088

    Article  PubMed  CAS  Google Scholar 

  34. Cha EK, Shariat SF, Kormaksson M, Novara G, Chromecki TF, Scherr DS et al (2012) Predicting clinical outcomes after radical nephroureterectomy for upper tract urothelial carcinoma. Eur Urol 61:818–825

    Article  PubMed  Google Scholar 

  35. Messer JC, Terrell JD, Herman MP, Ng CK, Scherr DS, Scoll B, et al (2011) Multi-institutional validation of the ability of preoperative hydronephrosis to predict advanced pathologic tumor stage in upper-tract urothelial carcinoma. Urol Oncol. http://dx.doi.org/10.1016/j.urolonc.2011.07.011

  36. Margulis V, Youssef RF, Karakiewicz PI, Lotan Y, Wood CG, Zigeuner R et al (2010) Preoperative multivariable prognostic model for prediction of nonorgan confined urothelial carcinoma of the upper urinary tract. J Urol 184:453–458

    Article  PubMed  Google Scholar 

  37. Roupret M, Zigeuner R, Palou J, Boehle A, Kaasinen E, Sylvester R et al (2011) European guidelines for the diagnosis and management of upper urinary tract urothelial cell carcinomas: 2011 update. Eur Urol 59:584–594

    Article  PubMed  Google Scholar 

  38. Roupret M, Hupertan V, Seisen T, Colin P, Xylinas E, Yates DR, et al (2012) Prediction of cancer-specific survival after radical nephroureterectomy for upper tract urothelial carcinoma: development of an optimized post-operative nomogram using decision curve analysis. J Urol. doi:10.1016/j.juro.2012.10.057

Download references

Conflict of interest

The authors declare that they have no conflict of interest to disclose.

Author information

Authors and Affiliations

  1. Academic Department of Urology of la Pitié-Salpêtrière Hospital, 1 Assistance Publique-Hôpitaux de Paris, Faculté de Médecine Pierre et Marie Curie, University Paris VI, Hôpital Pitié, 47-83 bvd de l’Hôpital, 75013, Paris, France

    Sarah J. Drouin, David R. Yates & Morgan Rouprêt

  2. Academic Department of Urology of Bichat Hospital, 1 Assistance Publique-Hôpitaux de Paris, Paris, France

    Vincent Hupertan

  3. Academic Department of Urology of la Pitié-Salpêtrière Hospital, 1 Assistance Publique-Hôpitaux de Paris, Faculté de Médecine Pierre et Marie Curie, Institut Universitaire De Cancerologie GRC Oncotype, University Paris VI, Hôpital Pitié, 47-83 bvd de l’Hôpital, 75013, Paris, France

    Olivier Cussenot

Authors
  1. Sarah J. Drouin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David R. Yates
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Hupertan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olivier Cussenot
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Morgan Rouprêt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Morgan Rouprêt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Drouin, S.J., Yates, D.R., Hupertan, V. et al. A systematic review of the tools available for predicting survival and managing patients with urothelial carcinomas of the bladder and of the upper tract in a curative setting. World J Urol 31, 109–116 (2013). https://doi.org/10.1007/s00345-012-1008-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-012-1008-9

Keywords

Search

Navigation