Elsevier

European Urology

Volume 68, Issue 2, August 2015, Pages 238-253
European Urology

Platinum Priority – Collaborative Review – Bladder Cancer
Editorial by Jo Cresswell and A. Hugh Mostafid on pp. 254–255 of this issue
Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature

https://doi.org/10.1016/j.eururo.2015.01.032Get rights and content

Abstract

Context

This review focuses on risk assessment and prediction tools for bladder cancer (BCa).

Objective

To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa.

Evidence acquisition

A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool.

Evidence synthesis

Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non–muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa.

Conclusions

Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care.

Patient summary

We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making.

Introduction

Bladder cancer (BCa) is a heterogeneous disease with high prevalence and recurrence rates [1], [2], [3]. Prognostication and risk assessment are essential for treatment decision making, patient counseling, and inclusion in clinical trials. Outcome prediction based on a physician's experience alone may be subjectively influenced [4], [5]. Most commonly, decisions that involve prediction are based on risk categories. Cancer stage represents the simplest and most commonly used example of a prediction tool. The American Joint Committee on Cancer (AJCC) TNM staging system has been validated and is used widely to predict the risk of disease recurrence in patients treated with radical cystectomy (RC) [6]. These staging systems provide useful estimates of survival outcome, with more aggressive treatments reserved for patients with higher stage disease. However, current staging systems have been shown to be less accurate at prediction than prediction models that incorporate several clinical variables; furthermore, staging systems cannot easily incorporate novel information such as molecular markers or more complex bioinformatics. In addition to standard oncologic features, patients with BCa are generally elderly and have significant comorbidities, resulting in the need for competing-risk analyses to be able to choose individualized therapies.

All of this complex information requires tools that can integrate multiple disparate data points for each individual patient to allow personalized medicine. In recent years, a plethora of published papers have described different prediction tools relying on common pre- and postoperative clinical and pathologic parameters in BCa. Their goal is to facilitate and improve daily clinical practice though integration of evidence-based information or data [7], [8], [9]. However, the vast majority of clinical decisions for BCa are still made without reference to prediction tools or any type of decision aid [10]. A possible explanation for the low adoption rate of prediction tools is that they have not been demonstrated to improve clinical decision making for specific clinical decisions on external validation. Another explanation is that prediction tools have rarely been integrated into electronic medical records so that they are available to the doctor at the point of care.

In patients with non–muscle-invasive bladder cancer (NMIBC), prediction tools could have an impact on the decision-making process regarding surveillance schedules and administration of intravesical therapy (immediate postoperative instillation of chemotherapy (IPIC) and/or adjuvant chemotherapy [11]. Accurate preoperative prediction tools could help predict risks of progression, enabling better selection of clinical T1 high-grade patients who should undergo RC as primary treatment (early RC) compared with intravesical bacillus Calmette-Guérin (BCG). In patients with high-risk NMIBC and muscle-invasive bladder cancer (MIBC) undergoing RC, accurate prediction of the presence of lymph node (LN) metastasis and the probability of disease recurrence could provide guidance for selecting patients who have an imperative need for perioperative systemic chemotherapy integrated with extended LN dissection at the time of RC [12]. These scenarios are examples of common crossroads or dilemmas in the daily management of patients with BCa.

We have analyzed and identified the best clinical tools and scenarios for use with prognostic and prediction models in managing BCa and proposed a pathway for their validation and integration into clinical practice.

Section snippets

Evidence acquisition

A literature search of the English literature was performed using PubMed in July 2013 using the keywords bladder cancer, radical cystectomy, prediction, predictive tool, nomogram, risk grouping, risk table, decision curve, decision tool, and prognosis. Before circulating the last draft, we performed a final literature search in March 2014 to add any meaningful references. More than 1600 publications were retrieved. Relevant papers were preselected by two authors (L.A.K. and S.F.S.), and all

Currently available prediction tools

We provide an overview of the currently available prediction tools for BCa. We present the prediction tools by specific clinical problems or questions in NMIBC, MIBC, and metastatic BCa, summarizing predictor variables, the number of patients used for development, tool-specific features, discrimination, calibration, and whether internal and/or external validation was performed (Table 1).

Prediction of disease recurrence and progression in patients with non–muscle-invasive bladder cancer [7,13–24]

In a large study cohort of 1529 patients with NMIBC, Millan-Rodriguez et al examined predictors of disease

Conclusions

Prognostic and prediction tools with high discriminative accuracy may help facilitate the decision-making process and potentially improve clinical outcomes for BCa patients, but these tools need external validation to show good calibration and improve outcome on indices that are of direct relevance for making clinical decisions, such as the PPV, and especially decision analysis (net benefit). Few tools have met these criteria. Integration of complex data such as genomics and epigenetics are

References (106)

  • J. Fernandez-Gomez et al.

    Predicting nonmuscle invasive bladder cancer recurrence and progression in patients treated with bacillus Calmette-Guerin: the CUETO scoring model

    J Urol

    (2009)
  • B.W.G. van Rhijn et al.

    Molecular grade (FGFR3/MIB-1) and EORTC risk scores are predictive in primary non-muscle-invasive bladder cancer

    Eur Urol

    (2010)
  • J. Fernandez-Gomez et al.

    The EORTC tables overestimate the risk of recurrence and progression in patients with non-muscle-invasive bladder cancer treated with bacillus Calmette-Guérin: external validation of the EORTC risk tables

    Eur Urol

    (2011)
  • H.M. Rosevear et al.

    Usefulness of the Spanish Urological Club for Oncological Treatment scoring model to predict nonmuscle invasive bladder cancer recurrence in patients treated with intravesical bacillus Calmette-Guerin plus interferon-alpha

    J Urol

    (2011)
  • R.J. Lammers et al.

    NMIBC risk calculators: how useful are they for the practicing urologist and how can their clinical utility be improved?

    Urol Clin North Am

    (2013)
  • F. Koga et al.

    Significance of positive urine cytology on progression and cancer-specific mortality of non--muscle-invasive bladder cancer

    Clin Genitourin Cancer

    (2014)
  • S.C. Smith et al.

    A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment

    Lancet Oncol

    (2011)
  • S.F. Shariat et al.

    Clinical nodal staging scores for bladder cancer: a proposal for preoperative risk assessment

    Eur Urol

    (2012)
  • S.F. Shariat et al.

    Pathologic nodal staging score for bladder cancer: a decision tool for adjuvant therapy after radical cystectomy

    Eur Urol

    (2013)
  • J. Leissner et al.

    Extended radical lymphadenectomy in patients with urothelial bladder cancer: results of a prospective multicenter study

    J Urol

    (2004)
  • H. Isbarn et al.

    A population based assessment of perioperative mortality after cystectomy for bladder cancer

    J Urol

    (2009)
  • T.M. Morgan et al.

    Predicting the probability of 90-day survival of elderly patients with bladder cancer treated with radical cystectomy

    J Urol

    (2011)
  • E. Solsona et al.

    Risk groups in patients with bladder cancer treated with radical cystectomy: statistical and clinical model improving homogeneity

    J Urol

    (2005)
  • S.F. Shariat et al.

    Predictive value of combined immunohistochemical markers in patients with pT1 urothelial carcinoma at radical cystectomy

    J Urol

    (2009)
  • S.F. Shariat et al.

    Combination of multiple molecular markers can improve prognostication in patients with locally advanced and lymph node positive bladder cancer

    J Urol

    (2010)
  • G. Sonpavde et al.

    Prognostic risk stratification of pathological stage T3N0 bladder cancer after radical cystectomy

    J Urol

    (2011)
  • S.F. Shariat et al.

    Risk stratification of organ confined bladder cancer after radical cystectomy using cell cycle related biomarkers

    J Urol

    (2012)
  • A. Buchner et al.

    Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks

    Eur J Surg Oncol

    (2013)
  • Y. Lotan et al.

    Prospective evaluation of a molecular marker panel for prediction of recurrence and cancer-specific survival after radical cystectomy

    Eur Urol

    (2013)
  • M.S. Eisenberg et al.

    The SPARC score: a multifactorial outcome prediction model for patients undergoing radical cystectomy for bladder cancer

    J Urol

    (2013)
  • B.C. Baumann et al.

    A novel risk stratification to predict local-regional failures in urothelial carcinoma of the bladder after radical cystectomy

    Int J Radiat Oncol Biol Phys

    (2013)
  • G. Ploussard et al.

    Conditional survival after radical cystectomy for bladder cancer: evidence for a patient changing risk profile over time

    Eur Urol

    (2014)
  • J.A. Karam et al.

    Use of combined apoptosis biomarkers for prediction of bladder cancer recurrence and mortality after radical cystectomy

    Lancet Oncol

    (2007)
  • L.C. Wang et al.

    Combining smoking information and molecular markers improves prognostication in patients with urothelial carcinoma of the bladder

    Urol Oncol

    (2014)
  • A. Stenzl et al.

    Treatment of muscle-invasive and metastatic bladder cancer: update of the EAU guidelines

    Eur Urol

    (2011)
  • S.F. Shariat et al.

    Outcomes of radical cystectomy for transitional cell carcinoma of the bladder: a contemporary series from the Bladder Cancer Research Consortium

    J Urol

    (2006)
  • R. Mayr et al.

    Comorbidity and performance indices as predictors of cancer-independent mortality but not of cancer-specific mortality after radical cystectomy for urothelial carcinoma of the bladder

    Eur Urol

    (2012)
  • T. Nakagawa et al.

    Prognostic risk stratification of patients with urothelial carcinoma of the bladder with recurrence after radical cystectomy

    J Urol

    (2013)
  • M. Ploeg et al.

    Prognostic factors for survival in patients with recurrence of muscle invasive bladder cancer after treatment with curative intent

    Clin Genitourin Cancer

    (2011)
  • J.R. Gregg et al.

    Effect of preoperative nutritional deficiency on mortality after radical cystectomy for bladder cancer

    J Urol

    (2011)
  • P.L. Ross et al.

    Comparisons of nomograms and urologists’ predictions in prostate cancer

    Semin Urol Oncol

    (2002)
  • M.C. Specht et al.

    Predicting nonsentinel node status after positive sentinel lymph biopsy for breast cancer: clinicians versus nomogram

    Ann Surg Oncol

    (2005)
  • S.B. Edge et al.

    The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM

    Ann Surg Oncol

    (2010)
  • R.J. Sylvester et al.

    Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials

    Eur Urol

    (2006)
  • P.I. Karakiewicz et al.

    Precystectomy nomogram for prediction of advanced bladder cancer stage

    Eur Urol

    (2006)
  • J.W. Catto et al.

    Neurofuzzy modeling to determine recurrence risk following radical cystectomy for nonmetastatic urothelial carcinoma of the bladder

    Clin Cancer Res

    (2009)
  • K. Chamie et al.

    Compliance with guidelines for patients with bladder cancer: variation in the delivery of care

    Cancer

    (2011)
  • J.W. Catto et al.

    Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks

    Clin Cancer Res

    (2003)
  • K. Fujikawa et al.

    Predicting disease outcome of non-invasive transitional cell carcinoma of the urinary bladder using an artificial neural network model: results of patient follow-up for 15 years or longer

    Int J Urol

    (2003)
  • S.J. Hong et al.

    Nomograms for prediction of disease recurrence in patients with primary Ta, T1 transitional cell carcinoma of the bladder

    J Korean Med Sci

    (2008)
  • Cited by (229)

    View all citing articles on Scopus

    Please visit www.eu-acme.org/europeanurology to read and answer questions on-line. The EU-ACME credits will then be attributed automatically.

    View full text