Platinum Priority – Collaborative Review – Bladder CancerEditorial 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

Luis A. Kluth a,b, Peter C. Black c, Bernard H. Bochner d, James Catto e, Seth P. Lerner f,Arnulf Stenzl g, Richard Sylvester h, Andrew J. Vickers i, Evanguelos Xylinas a,j,Shahrokh F. Shariat a,k,l,m,*

a Department of Urology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA; b Department of Urology, University Medical-

Center Hamburg-Eppendorf, Hamburg, Germany; c Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada; d Department of

Urology, Memorial Sloan-Kettering Cancer Center, Kimmel Center for Prostate and Urologic Tumors, New York, NY, USA; e Academic Urology Unit, University

of Sheffield, Sheffield, UK; f Scott Department of Urology, Baylor College of Medicine, Houston, TX, USA; g Department of Urology, Eberhard-Karls University,

Tuebingen, Germany; h EORTC Headquarters, Brussels, Belgium; i Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center,

New York, NY, USA; j Department of Urology, Cochin Hospital, Assistance Publique-Hopitaux de Paris, Paris Descartes University, Paris, France; k Department

of Urology, Medical University of Vienna, Vienna, Austria; l Department of Urology, UT Southwestern, Dallas, TX, USA; m Division of Medical Oncology, Weill

Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3

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Article info

Article history:Accepted January 30, 2015

Keywords:Bladder cancerRadical cystectomyOutcomeDisease recurrenceSurvivalPrediction toolPrognostic toolNomogramPredictionPrognosis

Please visit www.eu-acme.org/europeanurology to read andanswer questions on-line.The EU-ACME credits willthen be attributedautomatically.

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 toenhance clinical decision making and counseling of patients with BCa.Evidence acquisition: A literature search in English was performed using PubMed in July2013. Relevant risk assessment and prediction tools for BCa were selected. More than1600 publications were retrieved. Special attention was given to studies that investi-gated the clinical benefit of a prediction tool.Evidence synthesis: Most prediction tools for BCa focus on the prediction of diseaserecurrence and progression in non–muscle-invasive bladder cancer or disease recur-rence and survival after radical cystectomy. Although these tools are helpful, recentprediction tools aim to address a specific clinical problem, such as the prediction oforgan-confined disease and lymph node metastasis to help identify patients who mightbenefit from neoadjuvant chemotherapy. Although a large number of prediction toolshave been reported in recent years, many of them lack external validation. Few studieshave investigated the clinical utility of any given model as measured by its ability toimprove clinical decision making. There is a need for novel biomarkers to improve theaccuracy 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 needexternal validation and assessment of clinical utility before they can be incorporatedinto routine clinical care.

* Corresponding author. Department of Urology, Medical University of Vienna, Vienna GeneralHospital, Wahringer Gurtel 18–20, 1090 Vienna, Austria. Tel. +43 1 404 00 2616;Fax: +43 1 404 002332.E-mail address: sfshariat@gmail.com (S.F. Shariat).

http://dx.doi.org/10.1016/j.eururo.2015.01.0320302-2838/# 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

1. Introduction

Bladder cancer (BCa) is a heterogeneous disease with highprevalence and recurrence rates [1–3]. Prognostication andrisk assessment are essential for treatment decision making,patient counseling, and inclusion in clinical trials. Outcomeprediction based on a physician’s experience alone may besubjectively influenced [4,5]. Most commonly, decisionsthat involve prediction are based on risk categories. Cancerstage represents the simplest and most commonly usedexample of a prediction tool. The American Joint Committeeon Cancer (AJCC) TNM staging system has been validatedand is used widely to predict the risk of disease recurrencein patients treated with radical cystectomy (RC) [6]. Thesestaging systems provide useful estimates of survivaloutcome, with more aggressive treatments reserved forpatients with higher stage disease. However, currentstaging systems have been shown to be less accurate atprediction than prediction models that incorporate severalclinical variables; furthermore, staging systems cannoteasily incorporate novel information such as molecularmarkers or more complex bioinformatics. In addition tostandard oncologic features, patients with BCa are generallyelderly and have significant comorbidities, resulting in theneed for competing-risk analyses to be able to chooseindividualized therapies.

All of this complex information requires tools that canintegrate multiple disparate data points for each individualpatient to allow personalized medicine. In recent years, aplethora of published papers have described differentprediction tools relying on common pre- and postoperativeclinical and pathologic parameters in BCa. Their goal is tofacilitate and improve daily clinical practice thoughintegration of evidence-based information or data [7–9].However, the vast majority of clinical decisions for BCa arestill made without reference to prediction tools or any typeof decision aid [10]. A possible explanation for the lowadoption rate of prediction tools is that they have not beendemonstrated to improve clinical decision making forspecific clinical decisions on external validation. Anotherexplanation is that prediction tools have rarely beenintegrated into electronic medical records so that theyare 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 thedecision-making process regarding surveillance schedulesand administration of intravesical therapy (immediatepostoperative instillation of chemotherapy (IPIC) and/oradjuvant chemotherapy [11]. Accurate preoperative

prediction tools could help predict risks of progression,enabling better selection of clinical T1 high-grade patientswho should undergo RC as primary treatment (early RC)compared with intravesical bacillus Calmette-Guerin (BCG).In patients with high-risk NMIBC and muscle-invasivebladder cancer (MIBC) undergoing RC, accurate predictionof the presence of lymph node (LN) metastasis and theprobability of disease recurrence could provide guidance forselecting patients who have an imperative need forperioperative systemic chemotherapy integrated withextended LN dissection at the time of RC [12]. Thesescenarios are examples of common crossroads or dilemmasin the daily management of patients with BCa.

We have analyzed and identified the best clinical toolsand scenarios for use with prognostic and predictionmodels in managing BCa and proposed a pathway for theirvalidation and integration into clinical practice.

2. Evidence acquisition

A literature search of the English literature wasperformed using PubMed in July 2013 using the keywordsbladder cancer, radical cystectomy, prediction, predictivetool, nomogram, risk grouping, risk table, decision curve,decision tool, and prognosis. Before circulating the lastdraft, we performed a final literature search in March2014 to add any meaningful references. More than 1600publications were retrieved. Relevant papers were pre-selected by two authors (L.A.K. and S.F.S.), and all authorsfine-tuned and enhanced the list of papers to be included(Fig. 1).

3. Evidence synthesis

3.1. Currently available prediction tools

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

3.2. 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 recurrence,

Patient summary: We looked at models that aim to predict outcomes for patients withbladder cancer (BCa). We found a large number of prediction models that hold thepromise of facilitating treatment decisions for patients with BCa. However, many modelsare missing confirmation in a different patient cohort, and only a few studies have testedthe clinical utility of any given model as measured by its ability to improve clinicaldecision making.# 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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progression, and cancer-specific mortality (CSM) and thusdeveloped a risk stratification based on tumor multifocality,tumor size, intravesical BCG therapy, and the presence ofconcomitant carcinoma in situ (CIS) [15]. Tumor grade wasthe most powerful predictor of disease progression andCSM.

To predict the short- and long-term probabilities ofdisease recurrence and progression, the European Organi-zation for Research and Treatment of Cancer (EORTC)Genito-Urinary Cancers Group developed a scoring systemand risk tables [7]. This prediction tool was built on datafrom 2596 patients diagnosed with Ta/T1 tumors who wererandomized in seven previous EORTC Genito-UrinaryCancers Group trials. The scoring system was based onthe six most relevant clinical and pathologic predictors ofoutcomes: tumor stage and grade, number of tumors, tumorsize, concomitant CIS, and history of prior disease recur-rence. However, the data were limited by the low number ofpatients treated with BCG (7%) and IPIC (<10%), and the factthat no second-look transurethral resection (re-TUR) of thebladder was performed.

The Club Urologico Espanol de Tratamiento Oncologico(CUETO) recognized these flaws and developed a scoringmodel that predicted disease recurrence and progression in

1062 patients with NMIBC all treated with BCG from fourCUETO trials (that compared the efficacy of differentintravesical BCG treatments) [21]. The scoring systemwas based on seven factors: age, gender, prior recurrencestatus, number of tumors, tumor stage, tumor grade, andthe presence of concomitant CIS. Although patients in thesestudies received 12 BCG instillations for 5–6 mo, the studywas limited by the fact that current treatment practicessuch as IPIC, re-TUR, or BCG maintenance therapy were notperformed and could have had an impact on the outcomes.

Both the EORTC risk tables [25–30] and the CUETOscoring model [31,32] were externally validated andrecommended by international guidelines [2,3]. However,as Lammers et al recognized [33], the clinical benefit ofthese models has to be considered critically because theyprovide a low positive predictive value (PPV) for progres-sion, especially in patients with high-grade disease (EORTC:21%; CUETO: 24%). To improve the clinical utility of theEORTC and CUETO models in daily practice, it would also beimportant to update these models with previously unavail-able data that represent present standards of care such asIPIC, re-TUR, and fluorescence cystoscopy [33].

Xylinas et al evaluated the discrimination and calibrationof the EORTC risk tables and the CUETO scoring model in a

[(Fig._1)TD$FIG]

Fig. 1 – Flow diagram of evidence acquisition in a systematic review on prognostic and prediction models for bladder cancer.

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Table 1 – Available prognostic and prediction tools in bladder cancer

Study Prediction form Patient population Outcome No. of patients Variables Accuracy Validation

Prediction of disease recurrence and progression in patients with NMIBC

Parmar et al [13] Risk grouping NMIBC RFS 919 No. of tumors and cystoscopy at 3 mo NR NRQureshi et al [14] ANN Ta T1

Ta T1

T2–T4

6-mo RFS

PFS

1-yr CSS

56

105

56

EGFR, c-erbB2, p53, T stage, tumorgrade, tumor size, no. of tumors,gender, smoking status, histology ofmucosal biopsies, CIS, metaplasia,architecture, tumor location

75%

80%

82%

Internal

Millan-Rodriguezet al [15]

Riskstratification

NMIBC RFS, PFS, and CCS 1529 No. of tumors, tumor size, T stage,tumor grade, CIS, intravesical BCG

NR Notperformed

Catto et al [16] NFM and ANNs Ta–T4 RFS 109 p53, mismatch repair proteins, Tstage, tumor grade, age, smoking,previous cancer

88–95% Internal

Fujikawa et al [17] ANNs Ta T1 RFS and PFS 90 T stage, tumor grade, no. of tumors,age, gender, tumor architecture

No predictionof RFS possible;PPV and NPV forPFS: 40% and100%, respectively

Internal

Shariat et al [18] Probabilitynomogram

NMIBC RFS and PFS 2681 Age, gender, urine cytology,dichotomized NMP22 level (andinstitution)

84% for RFS ofany BCa; 87% forPFS of Ta/T1high-grade BCa orCIS; 86% for PFSof stage T2 orhigher BCa

Internal

Catto et al [19] NFM and ANN NMIBC Presence andtiming of diseaseprogression

117 T stage, tumor grade, age, gender,smoking status; p53 expression,methylation status of 11 loci

NFM: 94–100%ANN: 88–90%

Internal

Sylvester et al [7] Look-up table NMIBC 1-yr and 5-yr RFS1-yr and 5-yr PFS

2596 No. of tumors, tumor size, priorrecurrence rate, T stage, tumor grade,concomitant CIS

64% and 64%74% and 75%

Internal and external

Hong et al [20] Probabilitynomogram

PrimaryTa T1

3-yr and 5-yr RFS 1587 Age, tumor size, multiplicity, tumorgrade, concomitant CIS, IVT

60% Internal

Fernandez-Gomezet al [21]

Look-up table NMIBC 1-yr and 5-yr RFS1-yr and 5-yr PFS

1062 Age, gender, T stage, T grade, priorrecurrence rate, multiplicity, andconcomitant CIS

64%, both69% and 70%

Internal and external

Pan et al [22] Probabilitynomogram

Ta T1 RFSPFSCSS

1366 Tumor grade, T stage, age, IVT 66%79%87%

Internal

Yamada et al [23] Probabilitynomogram

PrimaryNMIBC

RFSPFS

800 No. of tumors, tumor shape, tumorgrade, IVT

61%71%

Internal andexternal

Ali-El-Deinet al [24]

Probabilitynomogram

NMIBC 1-yr and 5-yr RFS1-yr and 5-yr PFS

1019 Age, sex, stage, tumor grade,concomitant CIS, tumor size,multiplicity, macroscopic appearanceof the tumor, history of tumorrecurrence, type of IVT

65% and 69%70% and 74%

Internal

Prediction of non–organ-confined disease

Karakiewiczet al [8]

Probabilitynomogram

NMIBC and MIBC RC T and N stage 731 Age, TUR stage, TUR grade, CIS 76% T stage63% N stage

Internal

Margel et al [35] Principal componentanalysis

Primary BCa(!cT2N0M0)

Clinicallyorgan-confineddisease

133 CEA, CA 125, CA 19-9, clinical stage,hydronephrosis, CIS, tumor size >3cm

85% External

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Table 1 (Continued )

Study Prediction form Patient population Outcome No. of patients Variables Accuracy Validation

Xie et al [36] Probabilitynomogram

!cT2N0M0 Non–organ-confineddisease (pT3–4 orpN+)

248 Age, gender, recurrent frequency,tumor size, no. of tumors,hydronephrosis, T stage,tumor grade, LVI, CIS

79% Internal

Green et al [37] Probabilitynomogram

!cT2N0M0 Non–organ-confineddisease (pT3/Nanyor pTany/N+)

201 T stage, presence of LVI, radiographicevidence of non–organ-confined BCaor hydronephrosis

83% Internal

Mitra et al [38] Decision tree model cT2N0M0 Pathologicupstaging,RFS, and OS

948 Age, hydronephrosis, deepmuscle involvement, tumorgrowth pattern

66% (pathologicupstaging)

Internal

Ahmadi et al [39] Principal componentanalysis

!cT2N0M0 Pathologic stage atRC

1186 Age, clinical stage, no. ofintravesical treatments, LVI,multiplicity of tumors,hydronephrosis, and palpablemass

68% Internal

Prediction of response to neoadjuvant chemotherapy

Takata et al [44] Gene expressionmodel

T2a–T3bN0M0 Response to MVACchemotherapy. RFSat 3 yr

40 14 genes identified: 9 respondersand 9 nonresponders

PPV 79% Internal

Prediction of nodal metastasis at RC

Smith et al [46] Gene expressionmodel

cTanyN0M0 LN metastasis 341 20-gene panel: stratification ofpatients into low or high risk of LVI

UAC: 67% Internal and external

Shariat et al [47] Clinical nodalstaging score

RC LN status 4335 No. of LNs removed, no. of positiveLNs, pathologic T stage, LVI

NR Internal and external

Shariat et al [48] Pathologic nodalstaging score

RC LN metastasis at RC 4335 T stage, no. of LNs removed, no. ofpositive LNs

NR Internal

Prediction of early complications at RC

Isbarn et al [55] Probabilitynomogram

Partial or RC Perioperativemortality

10 981 Age, gender, pathologic T stage,pathologic grade, type of surgery,year of surgery, histology

70% Internal and external

Morgan et al [56] Probabilitynomogram

RC 90-d postoperativesurvival

169 Age, clinical stage, CCI, albumin 75% Internal

Abdollah et al [57] Reference table RC Postoperativemortality

12 274 Age, CCI 70% Internal and external

Prediction of disease recurrence and survival

Solsona et al [58] Risk score RC CSS 298 Pathologic T stage, LN status,prostatic stroma invasion

AUC: 77% Internal

Karakiewicz et al [49] Probabilitynomogram

RC 2-, 5-, and 8-yr RFS 731 Age, pathologic T stage, nodalstatus, pathologic grade, LVI,CIS, adjuvant radiotherapy,adjuvant CTX, neoadjuvant CTX

78% External

Shariat et al [50] Probabilitynomogram

RC 2-, 5-, and8-yr CSS and OS

731 Age, pathologic T stage, nodalstatus, pathologic grade, LVI,adjuvant radiotherapy, adjuvantCTX, neoadjuvant CTX

73% (OS)79% (CSS)

External

Bochner et al [59] Probabilitynomogram

RC 5-yr RFS 9064 Age, gender, pathologic T stage,LN status, pathologic grade,histology, time from diagnosisto surgery

75% External

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Bassi et al [60] ANN RC 5-yr OS 369 Age, gender, pathologic T stage,LN status, pathologic grade, LVI,concomitant PCa, history ofupper tract UC

76% Internal

Shariat et al [61] Probabilitynomogram

RC 2-, 5-, and 8-yrRFS and CSS

191 p53, p21, pRB, p27, cyclin E1,gender, age, pathologic T stage,pathologic grade, LVI, concomitant CIS

83% (RFS)87% (CSS)

Internal

Catto et al [9] NFM RC 2- and 5-yr RFS 609 Gender, pathologic T stage,pathologic grade, concomitant CIS,LVI, STSM, administration of CTX

CI 0.92 Internal

Shariat et al [62] Risk stratification pT1 BCa RFS and CSS 80 Pathologic grade, LVI, LN status,p53, pRB, p27, survivin, Ki-67

64% (RFS)78% (CSS)

Internal

Mitra et al [63] Gene expressionmodel

RC 5-yr RFS5-yr OS

58 Four-gene panel: JUN, MAP2K6,STAT3, and ICAM1

Akaikeinformationcriterionreported only

Internal andexternal

Umbreit et al [64] Risk score RC Site-specific (upperurinary tract vsabdomen/pelvis vsthoracic region vsbone) RFS

1338 Pathologic T stage, ureteral margin,multifocality, LN status, no. of LNsremoved, prostatic involvement,radiation exposure, gross hematuria,obesity

69–72% Internal

Shariat et al [65] Probabilitynomogram

pT3–4N0 or pTany, N-positive BCa

RFS and CSS 692 Age, gender, pathologic T stage, LVI,no. of LNs removed, no. of positiveLNs, concomitant CIS, adjuvant CTX,p53, pRB, p21, p27

63–69% (RFS)63–70% (CSS)

Internal

Sonpavde et al [66] Risk stratification pT2N0 BCa 5-yr RFS 707 Gender, pathologic grade, LVI, no.of LNs, age

68% Internal

Sonpavde et al [67] Risk Stratification pT3N0 BCa 5-yr RFS 578 Age, pathologic grade, gender, LVI,STSM, no. of LNs, decade

66% External

Kim et al [68] Gene networkanalysis

MIBC Disease progression 128 Four-gene expression signature: IL1B,S100A8, S100A9, EGFR

NR Internal andexternal

Gakis et al [69] Risk stratification RC 3-yr CSS 246 Pathologic T stage, LN density, STSM,CRP level

79% Internal

Todenhofer et al [70] Risk stratification RC 3-yr CSS 258 Pathologic T stage, STSM,thrombocytosis

75% Internal

Koga et al [71] Risk stratification cT2–4aN0M0treatedwith initialchemoradiationtherapyand partial or RC

5-yr CSS 170 Pathologic T stage, nodal status,nodal yield, LVI

NR Internal

Gondo et al [72] Risk stratification RC CSS 194 Pathologic T stage, LVI, and STSM NR (onlyabstract available)

Internal

Ishioka et al [73] Probabilitynomogram

RC 6-mo and 1-yr CSS 223 ECOG performance status, presence ofvisceral metastasis, hemoglobin level,age, CRP level

79% (6 mo)77% (1 yr)

Internal

Riester et al [74] Gene expressionmodelwith probabilitynomogram

RC OS 93 Multiple genetic markers (ie,fibronectin 1, NNMT, POSTN, SMAD6).IBCNC nomogram: see above

CI 0.66 Internal and external

Shariat et al [75] Probabilitynomogram

RC RFS and CSS 272 Age, gender, pathologic T stage,pathologic grade, no. of LNs removed,LVI, concomitant CIS, p53, p27,p21, pRB

79% (RFS)81% (CSS)

Internal andexternal

Xylinas et al [76] Probabilitynomogram

RCpT1–3N0

2-, 5-, and 7-yrRFS and CSS

2145 Pathologic T stage, STSM, and LVI 2-, 5-, and 7-yr RFS:67%, 65%, and 64%,respectively2-, 5-, and 7-yr CSS:69%, 66%, and 66%,respectively

Internal and external

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Table 1 (Continued )

Study Prediction form Patient population Outcome No. of patients Variables Accuracy Validation

Rink et al [77] Probabilitynomogram

RC with pN1 2-yr RFS and CSS 381 Gender, pathologic T stage, STSM,LN density, adjuvant CTX

63% (RFS)66% (CSS)

Internal

Buchner et al [78] ANNs RC RFS, CSS, and OS 2111 Age, gender, pathologic T stage,pathologic grade, concomitant CIS,LN status, LVI

74% (RFS)76% (CSS)69% (OS)

Internal

Lotan et al [79] Biomarker panel RC RFS and CSS 216 p53, p21, p27, Ki-67, cyclin E,pathologic T stage, STSM, LVI, LNI,adjuvant CTX

82% (RFS)80% (CSS)

Internal

Eisenberg et al [80] Risk stratification RC 5-yr CSS 2403 Pathologic T stage, LN status,multifocality, LVI, CCI, ECOG PS,current smoking, hydronephrosis,adjuvant CTX

75% Internal

Baumann et al [81] Risk stratification RC Local regionalrecurrence

442 pT stage and no. of LNs removed NR Internal

Sejima et al [82] Risk stratification RC CSS 249 Hemoglobin level, CRP level,pathologic T stage, STSM, LN status

NR Internal

Prediction of survival in metastatic disease or after disease recurrence

Bajorin et al [92] Risk stratification Nonresectable ormetastaticUC patients treatedonMVAC CTX trials

OS 203 KPS and presence of visceralmetastasis

NR External

Bellmunt et al [93] Risk stratification Metastaticplatinum-refractoryUC patients

OS 370 ECOG PS, hemoglobin level, livermetastasis

NR External

Nakagawa et al [95] Risk stratification Disease recurrenceafter RC

OS 114 Time to disease recurrence, symptomsof recurrence, no. of metastaticorgans, and CRP level

NR Not performed

Apolo et al [98] Probabilitynomogram

Nonresectable ormetastaticUC patients

OS 308 KPS, presence of visceral metastasis,hemoglobin, and albumin

67% Internal and external

Galsky et al [104] Probabilitynomogram

Nonresectable ormetastaticUC patients oncisplatin-basedCTX trials

OS 399 No. of visceral metastasis, ECOG PS,leucocyte level, site of primary tumor,presence of LN metastasis

63% Internal and external

Kluth et al. [106] Risk stratification BCa patients withdiseaserecurrence

CSS 372 Time to disease recurrence and PS 69% internal

ANN = artificial neural network; AUC = area under the curve; BCa = bladder cancer; BCG = Bacillus Calmette-Guerin; CA = cancer antigen; CCI = Charlson Comorbidity Index; CEA = carcinoembryonic antigen;

CI = concordance index; CIS = carcinoma in situ; CRP = C-reactive protein; CSS = cancer-specific survival; CTX = chemotherapy; ECOG PS = Eastern Cooperative Oncology Group performance status; IVT = intravesical

therapy; KPS = Karnofsky Performance Status; LN = lymph node; LNI = lymph node involvement; LVI = lymphovascular invasion; MIBC = muscle-invasive bladder cancer; MVAC = methotrexate, vinblastine,

doxorubicin, and cisplatin; NFM = neurofuzzy model; NMIBC = non–muscle-invasive bladder cancer; NMP22 = nuclear matrix protein 22; NPV = negative predictive value; NR = not reported; OS = overall

survival; PCa = prostate cancer; PFS = progression-free survival; PPV = positive predictive value; RC = radical cystectomy; RFS = recurrence-free survival; STSM = soft tissue surgical margin; TUR = transurethral

resection; UAC = universal approximation capability; UC = urothelial carcinoma.

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arge contemporary multicenter cohort of 4689 NMIBCpatients [32]. The authors created Cox regression models forthe prediction of time to disease recurrence and progres-sion, incorporating calculated risk scores as a predictor.Calibration plots were used to compare the predicted andthe actual risk among each individual risk score and for eachsingle end point. The authors found that the EORTC risktables and the CUETO scoring system exhibited poordiscrimination for both disease recurrence (c-indices0.597 and 0.523, respectively) and progression (c-indices0.662 and 0.616, respectively). Both models overestimatedthe risk for disease progression, especially in high-riskNMIBC patients, which was also true in a subgroup analysisof those treated with BCG therapy.

The first nomogram for BCa was published in 2005 andestimated the risk of disease recurrence and progressionbased on a multi-institutional cohort of 2681 patients with ahistory of Ta, T1, or Tis BCa [18]. All patients had previoushistologically confirmed NMIBC and provided voided urinesamples for cytologic and nuclear matrix protein 22 (NMP22)analyses before undergoing cystoscopy. In cases of suspiciouscystoscopy or cytology, patients were further investigatedwith transurethral biopsies. Overall, 898 patients hadrecurrent BCa: 24% had grade 1; 43%, grade 2; and 33%,grade 3 tumors; 45% had Ta; 32%, T1 or CIS; and 23%, T2tumors. In multivariable analyses, age, urine cytology status,and urinary NMP22 level were independently associatedwith both outcomes. The discrimination of a model based onage, gender, and urine cytology significantly increased forboth outcomes when NMP22 level was included as a variable.

Whereas this nomogram predicts disease recurrence andprogression in surveillance patients with a previous history ofNMIBC [18], the EORTC risk tables [7] and the CUETO scoringmodel [21] predict the short- and long-term probabilities ofdisease recurrence and progression in newly diagnosedpatients or at the time of disease recurrence. In other words,these two prediction tools address and serve different yetcomplementary clinical questions and problems.

Because NMIBC usually progresses to MIBC after multi-ple recurrences and thus many clinicopathologic variablesare changeable at each disease recurrence, the concept oftime-dependent covariate analysis was introduced recently[34]. This analysis is more accurate than time-fixed analysis,which is used for most studies predicting the prognosis andoutcome of NMIBC patients. Koga et al evaluated theprognostic role of voided urine cytology in 326 patientswith TaT1 NIMBC who underwent 597 TURs of the bladderbetween 2000 and 2010 [34]. Within a median follow-up of46 mo, positive voided urine cytology was independentlyassociated with disease progression and CSM in both time-dependent and time-fixed models. The authors identifiedseven predictors for disease progression and cancer-specificmortality in time-dependent analyses compared with threepredictors in time-fixed models, respectively.

3.3. Prediction of non–organ-confined disease [8,35–39]

In the pre-RC setting, clinical staging is imprecise because ofdifferences in TUR technique, nonstandardized use of

restaging TUR, inaccuracy and variable use of preoperativeimaging, and variability in the pathologic evaluation.However, clinical staging remains a major determinant oftreatment decision making [40].

Karakiewicz et al developed a preoperative nomogram topredict advanced pathologic stage (pT3–4) and the presenceof LN metastasis based on a multicenter cohort of731 patients with both clinical and pathologic staging data[8]. Integration of patient age, clinical tumor stage, tumorgrade, and presence of CIS resulted in a discrimination of76% (area under the curve [AUC]) for predicting advancedpathologic stage compared with 71% when TUR stage alonewas used. In predicting LN metastasis, the nomogramshowed discrimination of 63% (AUC) when tumor stage andgrade were combined versus 61% when TUR stage alone wasused. One possible explanation for the lower discriminationof the model in predicting LN metastasis may be explainedby the heterogeneity in LN staging in this multicenter study.

In a similar fashion, Green et al developed a preoperativenomogram that predicts non–organ-confined BCa based ona single-institution contemporary cohort of 201 patientswith clinically organ-confined disease who underwent RCwith pelvic LN dissection (PLND) without neoadjuvantchemotherapy [37]. The authors found that clinical tumorstage, presence of lymphovascular invasion (LVI), andradiographic evidence of non–organ-confined BCa orhydronephrosis were independently associated with pT3/Nany BCa. Clinical tumor stage and the presence of LVIremained independent predictors of pT3/Nany or pTany/N+urothelial cancer (UC) of the bladder, for which the finalnomogram reached a discrimination of 83% (AUC).

When choosing a certain prediction tool from multipleones that assess the same clinical question, the mostimportant criterion for clinical use is whether the toolchanges decisions for the better. However, it may notnecessarily be the best model for your patient. Specificmodel criteria, such as inclusion and exclusion of certainfactors, do not allow the use of a prediction tool for patientswith different characteristics or who underwent differenttreatment modalities. If a prediction tool was developed, forexample, for a cohort in which patients with chemotherapywere excluded, predictions cannot be made for suchpatients. If a patient is missing one of several variables ofa nomogram (eg, evaluation of hydronephrosis; see Greennomogram), this prediction tool does not perform the sameway.

3.4. Prediction of clinical response to neoadjuvant

chemotherapy

Grossman et al demonstrated that patients with bothclinically organ-confined (cT2) and non–organ-confined(cT3–T4a) BCa benefited from neoadjuvant chemotherapy[41]. These findings were confirmed by another internationalmulticenter randomized trial [42]. In a systematic review andmeta-analysis of >3000 patients across 11 clinical trials, theAdvanced Bladder Cancer Meta-analysis Collaboration foundthat in patients with MIBC, neoadjuvant chemotherapyconfers a statistically significant 9% absolute improvement in

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disease-free survival and a 5% absolute improvement insurvival at 5 yr [43]. Whether one can risk-stratify inrecommending neoadjuvant chemotherapy will requireprospective testing and the validation of predictive toolsand biomarkers.

Takata et al developed a gene expression modelpredicting the response to methotrexate, vinblastine,doxorubicin, and cisplatin (MVAC) chemotherapy basedon TUR tumor biopsy materials from 27 patients with T2a–T3bN0M0 BCa who were expected to undergo RC as aprimary treatment using a complementary DNA microarraycomposed of 27 648 genes [44]. The authors identified14 genes that were expressed differently among nineresponders (downstaging after two courses of MVAC, pT1 oflower or T1 or lower) and nine nonresponders (upstagingafter two courses of MVAC, pT2 or higher or T2 or higher).Based on these gene expression profiles, a predictionscoring system for chemosensitivity was established. Theauthors updated their series (13 additional cases to theinitial 27) and used the prediction scores in 32 patients whohad available clinical data on outcome. Patients withnegative scores were associated with a statistically signifi-cant lower 3-yr disease recurrence-free survival rate (12%)compared with those with a positive prediction score (57%),which was also true for overall survival [45]. However, thestudy is limited by the small data set and needs to beexternally validated in a prospectively collected, largercohort of BCa patients to identify accurately those who arechemosensitive. Further refinement is needed in the clinicallaboratory setting to bring this test into routine clinical use.

3.5. Prediction using lymph node invasion and indication and

extent of pelvic lymph node dissection [46–48]

Multi-institutional series of patients treated with RC haveshown that approximately 80% of patients with pathologicLN metastasis experience disease recurrence comparedwith 30–50% of patients with extravesical disease andpathologically LN-negative disease [49–51]. If clinicallyLN-negative patients who additionally harbor a low risk ofLN metastasis could be identified preoperatively, they wouldrepresent an ideal group for neoadjuvant chemotherapy.

Smith and colleagues developed a 20-gene model basedon the expression of TUR tumor tissue to quantify the riskof LN metastasis in clinically LN-negative MIBC patientsbefore RC [46]. A training data set of 156 patients from twoindependent centers was used to develop the geneexpression model and determine cut-offs for LN metastasis.External validation was performed on 185 patients from aprospective randomized phase 3 trial evaluating twodifferent adjuvant chemotherapy regimens. The geneexpression model demonstrated 67% (AUC) discriminationfor identifying LN-negative and LN-positive patients in thevalidation cohort. Although this study presents the firstmodel to predict LN involvement using a molecularapproach in a clinically relevant cohort of BCa patients,several factors limit this model for clinical practice. Usingthe cut-offs to group patients into high and low riskaccording to their relative risk of LN involvement, the PPV

was low at 0.30 in the validation cohort [52]. Although thegene model was externally validated in a prospectivecohort, the clinical assay has not yet undergone analyticvalidation.

It has been suggested that in addition to LN status,the anatomic extent of PLND can have prognostic andtherapeutic implications [53,54]. In an effort to reduceunderstaging and maximize survival, several studies havetried to estimate the risk of LN metastasis and determine aminimum number of LNs needed to be removed at the timeof RC, thereby providing a guide for the extent of PLND [47].

3.6. Prediction of peri- and postoperative complications

Isbarn et al developed a nomogram that estimates 90-dmortality after RC. The authors first investigated 30-, 60-, and90-d mortality rates in 10 981 BCa patients treated with RCfrom the Surveillance Epidemiology and End Results data-base [55]. Using life tables, perioperative mortality rates were1.1%, 2.4%, and 3.9% at each time point, respectively. Age,gender, year of surgery, type of cystectomy (partial vsradical), tumor stage, tumor grade, and histologic subtypewere used to build univariable and multivariable logisticregression models using 5510 patients in the developmentcohort and 5471 patients in the validation cohort. Age, tumorstage, and histologic subtype were independently associatedwith 90-d mortality. When tumor grade was added, the finalnomogram resulted in discrimination of 0.701 for predicting90-d mortality after RC.

Similar to the model of Isbarn et al, Morgan et aldeveloped a nomogram that evaluated the perioperative90-d mortality in patients aged "75 yr treated with RC forBCa [56]. Of the 220 identified patients at a single center,169 patients had complete data and were used for analysis;of these, 10.7% of the patients died within 90 d. Using Coxregression analysis, the authors found only age andpreoperative albumin to be independently associated with90-d mortality. The final nomogram was built on these twopredictors and two additional (nonsignificant) variables,namely, the Charlson Comorbidity Index (CCI) and thepresence of muscle-invasive disease. The resulting c-indexwas 0.71. The study is limited by the fact that only patientsaged "75 yr were included.

Abdollah et al developed a reference table to predict thepostoperative mortality rate in patients treated with RC forBCa [57]. Using the nationwide inpatient sample database,12 274 patients were identified between 1998 and 2007 andsplit into two cohorts (development cohort with n = 6188 andvalidation cohort with n = 6086). Logistic regression analysiswas performed to investigate the association of age, sex, race,CCI, urinary diversion type, year of surgery, annual hospitalcaseload, location and teaching status of hospital, region, andbed size of hospital with postoperative mortality. However,after using a stepwise variable removal, only age and CCIremained independently associated with postoperativemortality and were included in the reference table. Themodel was externally validated and finally showed discrimi-nation of 70% (AUC). Unfortunately, no pathologic findingswere available, which represents a limitation of this study.

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3.7. Prediction of disease recurrence and survival after radical

cystectomy [9,49,50,58–82]

Several postoperative nomograms have been developed topredict the natural history of surgically treated BCa patients[49,50,59]. The Bladder Cancer Research Consortium (BCRC)developed three nomograms to determine the probabilitiesof disease recurrence, CSM, and all-cause mortality at 2, 5,and 8 yr after RC (available at www.nomogram.org) [49,50].The disease recurrence nomogram showed a c-index of 0.78and comprised pathologic features such as tumor stage andnodal status, pathologic tumor grade, presence of LVI andCIS at RC, as well as the administration of chemotherapy(either neoadjuvant, adjuvant, or both) and/or radiationtherapy. The discrimination of the nomograms for cancer-specific and all-cause mortality were 0.78 and 0.73,respectively.

The International Bladder Cancer Nomogram Consor-tium (IBCNC) published a postoperative nomogram pre-dicting the 5-yr risk of disease recurrence following RC andPLND (available at www.nomograms.org) based on the dataof >9000 patients from 12 centers (including the BCRCcenters) (Fig. 2) [59]. This nomogram included age, gender,tumor grade, pathologic tumor stage, histologic type, LNstatus, and time from diagnosis to surgery. The discrimina-tion (using the c-index) of the nomogram was 0.75 andsignificantly superior to the AJCC TNM staging (0.68) or thestandard pathologic grouping models (0.62).

Both these nomograms provide high discriminationand good calibration for disease recurrence and survival.However, they do not really help clinicians understandwhether use of a prediction would improve clinical

decisions for an individual patient in daily practice. Inoncology, clinical decisions often depend implicitly orexplicitly on predictions. These predictions normally arethought of in terms of risk; therefore, we act when a patientis considered at sufficiently high risk so that the benefits ofintervention in terms of reducing risk (eg, adjuvantchemotherapy) outweigh the harms in terms of toxicitiesand cost. Vickers and Elkin developed a statistical method toevaluate prediction models known as decision curveanalysis. It is based on the decision-theoretical theoremthat the threshold probability of a disease at which a patientwould decide for treatment is informative of patientpreferences concerning the relative value of avoidingunnecessary treatment compared with delaying or avoidingtreatment where it is indicated [83].

To evaluate the clinical benefit of the IBCNC nomogram[59], that is, whether this prediction tool would improveclinical decision making, Vickers et al performed a decisionanalysis on 4462 patients from the initial cohort withcomplete data available [84]. The authors were especiallyinterested to assess whether a patient should receiveadjuvant chemotherapy. For this purpose they comparedthe number of patients eligible for chemotherapy based onpathologic stage criteria (pT3/4/N+) and based on threedifferent cut-off levels calculated by the nomogram (10%,25%, and 70% risk of disease recurrence at 5 yr). Clinical netbenefit was assessed in two steps: first, calculatingthe number of disease recurrences by adding a constantrelative risk reduction (ie, effect of chemotherapy) to thebaseline risk among eligible patients; and second, combin-ing disease recurrences and treatment strategies, andweighting the latter by a number-needed-to-treat thresh-old (ie, the maximum number of patients a clinician wouldconsider treating to prevent one disease recurrence). Asgraphically shown in Figure 3, for a drug with a relative riskof 0.80, with which clinicians would treat !20 patients toprevent one disease recurrence, the use of the 25% thresholdwould result in 60 fewer chemotherapy treatments per1000 patients without any increase in disease recurrencerates. A nomogram cut-off outperformed pathologic tumorstage for chemotherapy in every scenario of drug effective-ness and tolerability.

Riester et al evaluated the microarray data of 93 patientswith BCa treated with RC at a single center to determinegene expression profiles [74]. When comparing NMIBC(n = 15) with MIBC samples (n = 78), a classifier identifiedMIBC tumors with a high accuracy of 89% (Fisher lineardiscriminant). There was no significant correlation withdisease recurrence. The authors developed a gene expres-sion panel of 19 genes that was validated in gene expressiondata from six independent BCa microarray data sets. Thegene expression panel was independently associated withoverall survival and improved the accuracy of the previous-ly mentioned and validated IBCNC nomogram. However,even the inclusion of the gene signature in the IBCNCnomogram led to a c-index of 0.66 only [59]. The strength ofthis study is the clinical validation of published geneexpression data sets in the form of a meta-analysis. Thestudy is limited by the possibility of overfitting, especially in

[(Fig._2)TD$FIG]

Fig. 2 – International bladder cancer postoperative nomogram of9064 patients treated with radical cystectomy (RC) for urothelialcarcinoma of the bladder, predicting 5-yr risk of disease recurrenceafter RC. Instructions for nomogram use: Locate the patient’s sex on thesex axis. Draw a straight line up to the point’s axis to determine howmany points toward recurrence the patient should receive. Repeat thisprocess for each of the remaining axes, drawing a straight line eachtime to the point’s axis. Sum the points received for each predictivevariable, and locate this number on the total points axis. Draw astraight line down from the total points to the 60-mo progression-freeprediction axis for the patient’s specific risk of remaining free fromdisease recurrence for 5 yr. Reproduced with permission from theAmerican Society of Clinical Oncology [59].DxToRC = date of diagnosis to RC; GX = grade unknown; NX = nodeunknown; PFP = progression-free probability; RC = radical cystectomy;SCC = squamous cell carcinoma; TCC = transitional cell carcinoma.

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3 247

the external small data sets of published gene expressions,and the use of the same data cohort for development andvalidation.

Mitra et al profiled the expression of 69 genes from58 patients with Ta–T4 BCa treated with RC and fivecontrols [63]. The authors developed a four-gene expressionpanel associated with both disease recurrence and survival.Based on the individual gene expression patterns withoutcome, whereas low or normal expression was found to

be favorable, overexpression was unfavorable. The studycohort was then divided into two groups: patients withfavorable (low or normal) expressions of three or moregenes (n = 35), and patients’ favorable expression of two orfewer genes (n = 21). Consequently, statistically significantdifferences were found between the groups for 5-yr diseaserecurrence–free and overall-free survival rates. The Akaikeinformation criterion was used to assess the discriminatoryability of this 4- gene panel and showed no inferioritycompared with an 8- and 11-gene model. The four-genemodel was externally validated in primary tumors of91 patients with BCa; however, the study is limited by itssmall sample size and thus requires validation in aprospective, large cohort.

Prediction tools that do not adjust for the time of diseaserecurrence–free survival may lead to underestimation ofcancer control because survival improves with increasingtime to disease recurrence [85]. Only a few studies haveadjusted for competing risks and investigated the relevanceof conditional survival in prediction tools [76].

Xylinas et al developed competing-risk nomograms thatpredict disease recurrence and CSM of chemotherapy-naivepT1–3N0 BCa patients (n = 2145) [76]. The discrimination ofthe multivariable models at 2, 5, and 7 yr for diseaserecurrence using c-indices were 0.674, 0.65, and 0.644,respectively; discrimination at 2, 5, and 7 yr for predictingCSM were 0.693, 0.664, and 0.655, respectively.

In contrast, Rink et al developed two nomogramspredicting disease recurrence and CSM based on 381patients with LN metastases from a multi-institutionalcohort of 4335 patients with BCa treated with RC and PLNDwithout preoperative chemotherapy or radiotherapy [77].Including gender, tumor stage, soft tissue surgical margin,LN density, and administration of adjuvant chemothera-py, the model showed AUCs of 0.63 and 0.66 for diseaserecurrence-free and cancer-specific survival, respectively.

Bassi et al developed an artificial neural network (ANN)utilizing gender, several pathologic features, and history ofupper tract UC as input variables for prediction of 5-yr all-cause survival after RC [60]. In a single-institution cohortof 369 patients, the discriminative accuracy of the ANN(c-index: 0.76; based on 12 variables) was slightly superiorto the logistic regression model that was based on only twostatistically significant variables (c-index: 0.75; stage andgrade). Unfortunately, the comparison of the accuracy ofboth models was performed on the same population.

Catto et al developed a neurofuzzy model (NFM), apromising artificial intelligence approach, to predict diseaserecurrence following RC and PLND in 609 LN-negative BCapatients who did not receive neoadjuvant or adjuvantchemotherapy [9]. Two NFMs were developed to predictthe risk (classifier: 0–100%) and timing of disease recurrence(predictor). For model development, the data were dividedinto 90% for training (of which 60% was learning and 30% forvalidation) and 10% for testing. To train the predictor modelonly, cases with actual tumor recurrence were used (n = 172).With a median follow-up of 73 mo, the NFM classifierdemonstrated a high prediction of disease recurrence(c-index 0.92); calibration plots demonstrated that the

[(Fig._3)TD$FIG]

Relative Risk

Relative Risk

NNT

Thre

shol

dNN

T Th

resh

old

25100

200

250

250

200

100

25

0.5 0.6 0.7 0.8 0.9

0.90.80.70.60.5

Administer to all radical cystectomy pa!ents

Administer to pa!ents with a 5-year probability of disease recurrence 10%

Administer to pa!ents with a 5-year probability of disease recurrence 25%

Administer to pa!ents with a 5-year probability of disease recurrence 70%

Administer to no pa!ents (interven!on does more harm than good)

Fig. 3 – The choice of optimal strategy for administering adjuvantchemotherapy to radical cystectomy is illustrated in (top) all patientsand (bottom) patients with transitional cell carcinoma. The shadedareas identify the optimal strategy for each combination of thenumber-needed-to-treat threshold and relative risk. Grey indicatesadminister to all radical cystectomy patients; lightest orange,administer to patients with a 5-year probability of disease recurrence10%; light orange, administer to patients with a 5-year probability ofdisease recurrence 25%; medium orange, administer to patients with a5-year probability of disease recurrence 70%; dark orange, administer tono patients (intervention does more harm than good). The specificity ofthe optimal strategy increases from top left to bottom right. Note thatthe conventional definition was inferior to all other strategies for everycombination of NNT threshold and RR and, thus, has no shaded regionon the illustrations. Reproduced with permission from John Wiley andSons [84].NNT = number needed to treat.

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3248

model does well over different disease recurrence riskprobabilities at various time points. The NFM predictoridentified the timing of disease recurrence with a medianerror of 8.15 mo.

Only a few studies have demonstrated a significantincrease of discrimination when biomarkers were added toestablished predictors [14,16,61,86]. One of these studies,for example, demonstrated in 191 pTa–3N0M0 patientsfollowing RC that the addition of a panel of five well-established cell cycle regulatory biomarkers (p53, pRB, p21,p27, and cyclin E1) improved the discrimination ofcompeting-risk nomograms based on the TNM stagingsystem for disease recurrence and CSM in these patients bya clinically significant margin of approximately 10%[61]. Two smaller studies added biomarkers to standardclinicopathologic features using ANN and NFM predictiontools [14,16]. Wang et al recently demonstrated in a study of588 patients who underwent RC and bilateral lymphadenec-tomy for BCa that the addition of four combined cell cyclemarkers (p53, p21, pRB, and p27) and smoking statussignificantly increased the discrimination of a clinicopatho-logic base model on disease recurrence (c-index: 0.783) andCSM (c-index: 0.786) for former and current versus never-smokers, respectively [87].

Prediction tools such as these, which incorporate patho-logic and molecular information, could form the basis forcounseling patients regarding their risk of disease recurrencefollowing surgery and for designing clinical trials to testadjuvant treatment strategies in high-risk patients.

3.8. Prediction of survival in metastatic disease or after disease

recurrence

Although the natural history of BCa from RC to diseaserecurrence has been intensively investigated [49,50,59,88–91], that of patients who have experienced diseaserecurrence after RC remains poorly understood.

Bajorin et al reported that two risk factors, a poorKarnofsky performance status (KPS) and the presence ofvisceral metastases, can stratify patients with nonresect-able or metastatic UC into separate risk groups with regardto overall mortality [92]. This and other studies [93,94]suggest that comorbidities are important and should betaken into account for predicting survival in both localizedand metastatic UC. Recent studies confirmed that not onlythe presence, but the number [95,96] and location [96] ofvisceral metastasis can predict outcomes in patients whoexperienced disease recurrence after RC. Although theBajorin et al risk grouping was validated in the setting ofprospective randomized trials [93,97], this risk stratifica-tion has been the only prediction model available forpatients with metastatic UC since 1999.

Apolo et al recently developed a nomogram that predictsoverall survival in 308 metastatic UC patients receivingcisplatin-based chemotherapy who were extracted fromseven prospective phase 2 trials. The objective was topredict the 1-, 2-, and 5-yr overall survival and to improveaccuracy over the Bajorin et al prediction model [98]. Thefinal model included the presence of visceral metastases,

KPS, albumin, and hemoglobin. This four-variable modelwas compared with the two-variable model of Bajorin et al[92], resulting in a favorable discrimination for the newlydeveloped model (c-index: 0.67 vs 0.63). Hypoalbumine-mia, however, is controversial as a nutritional markerbecause it can have a protracted course, and systemicfactors such as inflammation and stress can affect it [99]. Incontrast, low preoperative albumin is associated with ahigher risk of overall mortality in UCB patients after RC[100]. Although anemia is a variable associated with cancer-specific survival [101], hemoglobin levels of cancer patientsmay be confounded by the administration of bloodtransfusions or erythropoietin substitution [102,103]. Theauthors could not control for these factors, unfortunately,due to the study’s retrospective design.

Galsky et al published a similar pretreatment nomogrambased on 399 UC patients [104]. However, the study ofGalsky et al and the previous studies (Bajorin et al [92] andApolo et al [98]) included only a select group of patientswho were eligible for inclusion in clinical trials (ie, eligiblefor cisplatin-based chemotherapy), suggesting a largerburden of metastasis and more fit patients; thus thesemodels might not be generalizable to all metastatic UCpatients. Furthermore, the patients included in thesestudies present a heterogeneous cohort with unresectableand/or metastatic UC of both the lower and upper urinarytract [105].

Kluth et al aimed to evaluate the prognostic value of theBajorin et al criteria in a retrospective multi-institutionalstudy of 372 patients who experienced disease recurrenceafter RC for BCa [106]. The authors demonstrated thatcancer-specific survival at 1 yr was 79%, 76%, and 47% forpatients with no (n = 105), one (n = 180), and two (n = 87)risk factors ( p < 0.001; c-index: 0.604), thus confirming theprognostic value of the Bajorin et al criteria. On multivari-able analyses, KPS <80%, higher American Society ofAnesthesiologists score, anemia, leukocytosis, and shortertime to disease recurrence (all p values <0.034) wereindependently associated with increased CSM. Based on thecombination of time to disease recurrence and KPS, a modelwas developed to predict cancer-specific survival andresulted in an improved discrimination (c-index: 0.694).

Nakagawa et al recently developed a risk stratificationtool to predict survival in patients with disease recurrenceafter RC that was based on four factors: time to recurrence,symptoms of recurrence, number of metastatic organs, andC-reactive protein level [95]. However, this study wasclearly limited by its small number of patients (n = 114) andsingle-center design. The 1-yr cancer-specific survival ofthis cohort was 89%, 30%, and 12% for patients withfavorable (0–1 risk factors), intermediate (2 risk factors),and poor risk (3–4 risk factors). The clinical benefit of thisrisk stratification needs to be assessed using c-index andlarger data sets with longer follow-up.

4. Conclusions

Prognostic and prediction tools with high discriminativeaccuracy may help facilitate the decision-making process and

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3 249

potentially improve clinical outcomes for BCa patients, butthese tools need external validation to show good calibrationand improve outcome on indices that are of direct relevancefor making clinical decisions, such as the PPV, and especiallydecision analysis (net benefit). Few tools have met thesecriteria. Integration of complex data such as genomics andepigenetics are necessary to improve prediction and thusevidence-based medical decision making but needs proof ofits benefits in well-designed prospective studies.

Author contributions: Luis A. Kluth and Shahrokh F. Shariat had full

access to all the data in the study and take responsibility for the integrity

of the data and the accuracy of the data analysis.

Study concept and design: Kluth, Shariat.

Acquisition of data: Kluth, Shariat.

Analysis and interpretation of data: Kluth, Black, Bochner, Catto, Lerner,

Stenzl, Sylvester, Vickers, Xylinas, Shariat.

Drafting of the manuscript: Kluth, Shariat.

Critical revision of the manuscript for important intellectual content: Black,

Bochner, Catto, Lerner, Stenzl, Sylvester.

Statistical analysis: None.

Obtaining funding: None.

Administrative, technical, or material support: None.

Supervision: Shariat.

Other (specify): None.

Financial disclosures: Luis A. Kluth and Shahrokh F. Shariat certify that

all conflicts of interest, including specific financial interests and

relationships and affiliations relevant to the subject matter or materials

discussed in the manuscript (eg, employment/affiliation, grants or

funding, consultancies, honoraria, stock ownership or options, expert

testimony, royalties, or patents filed, received, or pending), are the

following: None.

Funding/Support and role of the sponsor: None.

References

[1] Burger M, Catto JW, Dalbagni G, et al. Epidemiology and risk

factors of urothelial bladder cancer. Eur Urol 2013;63:234–41.

[2] Burger M, Oosterlinck W, Konety B, et al. ICUD-EAU International

Consultation on Bladder Cancer 2012: non-muscle-invasive

urothelial carcinoma of the bladder. Eur Urol 2013;63:36–44.

[3] Babjuk M, Burger M, Zigeuner R, et al. EAU guidelines on non-

muscle-invasive urothelial carcinoma of the bladder: update 2013.

Eur Urol 2013;64:639–53.

[4] Ross PL, Gerigk C, Gonen M, et al. Comparisons of nomograms and

urologists’ predictions in prostate cancer. Semin Urol Oncol

2002;20:82–8.

[5] Specht MC, Kattan MW, Gonen M, et al. Predicting nonsentinel

node status after positive sentinel lymph biopsy for breast cancer:

clinicians versus nomogram. Ann Surg Oncol 2005;12:654–9.

[6] Edge SB, Compton CC. The American Joint Committee on Cancer:

the 7th edition of the AJCC cancer staging manual and the future of

TNM. Ann Surg Oncol 2010;17:1471–4.

[7] Sylvester RJ, van der Meijden AP, Oosterlinck W, 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;49:466–75,

discussion 475–7.

[8] Karakiewicz PI, Shariat SF, Palapattu GS, et al. Precystectomy

nomogram for prediction of advanced bladder cancer stage. Eur

Urol 2006;50:1254–60, discussion 1261–2.

[9] Catto JW, Abbod MF, Linkens DA, et al. Neurofuzzy modeling to

determine recurrence risk following radical cystectomy for non-

metastatic urothelial carcinoma of the bladder. Clin Cancer Res

2009;15:3150–5.

[10] Chamie K, Saigal CS, Lai J, et al. Compliance with guidelines for

patients with bladder cancer: variation in the delivery of care.

Cancer 2011;117:5392–401.

[11] Sylvester RJ, Oosterlinck W, van der Meijden AP. A single imme-

diate postoperative instillation of chemotherapy decreases the

risk of recurrence in patients with stage Ta T1 bladder cancer: a

meta-analysis of published results of randomized clinical trials.

J Urol 2004;171:2186–90, quiz 2435.

[12] Meeks JJ, Bellmunt J, Bochner BH, et al. A systematic review of

neoadjuvant and adjuvant chemotherapy for muscle-invasive

bladder cancer. Eur Urol 2012;62:523–33.

[13] Parmar MK, Freedman LS, Hargreave TB, et al. Prognostic factors

for recurrence and followup policies in the treatment of superficial

bladder cancer: report from the British Medical Research Council

Subgroup on Superficial Bladder Cancer (Urological Cancer Work-

ing Party). J Urol 1989;142:284–8.

[14] Qureshi KN, Naguib RN, Hamdy FC, et al. Neural network analysis

of clinicopathological and molecular markers in bladder cancer.

J Urol 2000;163:630–3.

[15] Millan-Rodriguez F, Chechile-Toniolo G, Salvador-Bayarri J, et al.

Primary superficial bladder cancer risk groups according to pro-

gression, mortality and recurrence. J Urol 2000;164:680–4.

[16] Catto JW, Linkens DA, Abbod MF, et al. Artificial intelligence in

predicting bladder cancer outcome: a comparison of neuro-fuzzy

modeling and artificial neural networks. Clin Cancer Res 2003;9:

4172–7.

[17] Fujikawa K, Matsui Y, Kobayashi T, 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;10:

149–52.

[18] Shariat SF, Zippe C, Ludecke G, et al. Nomograms including nuclear

matrix protein 22 for prediction of disease recurrence and pro-

gression in patients with Ta, T1 or CIS transitional cell carcinoma

of the bladder. J Urol 2005;173:1518–25.

[19] Catto JW, Abbod MF, Linkens DA, et al. Neuro-fuzzy modeling: an

accurate and interpretable method for predicting bladder cancer

progression. J Urol 2006;175:474–9.

[20] Hong SJ, Cho KS, Han M, et al. Nomograms for prediction of disease

recurrence in patients with primary Ta, T1 transitional cell carci-

noma of the bladder. J Korean Med Sci 2008;23:428–33.

[21] Fernandez-Gomez J, Madero R, Solsona E, et al. Predicting non-

muscle invasive bladder cancer recurrence and progression in

patients treated with bacillus Calmette-Guerin: the CUETO scor-

ing model. J Urol 2009;182:2195–203.

[22] Pan CC, Chang YH, Chen KK, et al. Constructing prognostic model

incorporating the 2004 WHO/ISUP classification for patients with

non-muscle-invasive urothelial tumours of the urinary bladder.

J Clin Pathol 2010;63:910–5.

[23] Yamada T, Tsuchiya K, Kato S, et al. A pretreatment nomogram

predicting recurrence- and progression-free survival for nonmus-

cle invasive bladder cancer in Japanese patients. Int J Clin Oncol

2010;15:271–9.

[24] Ali-El-Dein B, Sooriakumaran P, Trinh QD, et al. Construction of

predictive models for recurrence and progression in >1000

patients with non-muscle-invasive bladder cancer (NMIBC) from

a single centre. BJU Int 2013;111:E331–41.

[25] Hernandez V, De La Pena E, Martin MD, et al. External validation

and applicability of the EORTC risk tables for non-muscle-invasive

bladder cancer. World J Urol 2011;29:409–14.

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3250

[26] Sakano S, Matsuyama H, Takai K, et al. Risk group stratification to

predict recurrence after transurethral resection in Japanese

patients with stage Ta and T1 bladder tumours: validation study

on the European Association of Urology guidelines. BJU Int 2011;

107:1598–604.

[27] Seo KW, Kim BH, Park CH, et al. The efficacy of the EORTC scoring

system and risk tables for the prediction of recurrence and progres-

sion of non-muscle-invasive bladder cancer after intravesical bacil-

lus calmette-guerin instillation. Korean J Urol 2010;51:165–70.

[28] van Rhijn BWG, Zuiverloon TCM, Vis AN, et al. Molecular grade

(FGFR3/MIB-1) and EORTC risk scores are predictive in primary

non-muscle-invasive bladder cancer. Eur Urol 2010;58:433–41.

[29] Pillai R, Wang D, Mayer EK, et al. Do standardised prognostic

algorithms reflect local practice? Application of EORTC risk tables

for non-muscle invasive (pTa/pT1) bladder cancer recurrence and

progression in a local cohort. ScientificWorldJournal 2011;11:

751–9.

[30] Fernandez-Gomez J, Madero R, Solsona E, et al. 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 2011;60:423–30.

[31] Rosevear HM, Lightfoot AJ, Nepple KG, 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;185:67–71.

[32] Xylinas E, Kent M, Kluth L, et al. Accuracy of the EORTC risk tables

and of the CUETO scoring model to predict outcomes in non-

muscle-invasive urothelial carcinoma of the bladder. Br J Cancer

2013;109:1460–6.

[33] Lammers RJ, Sylvester RJ, Lee CT, 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;40:155–64.

[34] Koga F, Kobayashi S, Fujii Y, et al. Significance of positive urine

cytology on progression and cancer-specific mortality of non–

muscle-invasive bladder cancer. Clin Genitourin Cancer 2014;12:

e87–93.

[35] Margel D, Harel A, Yossepowitch O, et al. A novel algorithm to

improve pathologic stage prediction of clinically organ-confined

muscle-invasive bladder cancer. Cancer 2009;115:1459–64.

[36] Xie HY, Zhu Y, Yao XD, et al. Development of a nomogram to

predict non-organ-confined bladder urothelial cancer before radi-

cal cystectomy. Int Urol Nephrol 2012;44:1711–9.

[37] Green DA, Rink M, Hansen J, et al. Accurate preoperative prediction

of non-organ-confined bladder urothelial carcinoma at cystec-

tomy. BJU Int 2013;111:404–11.

[38] Mitra AP, Skinner EC, Miranda G, et al. A precystectomy decision

model to predict pathological upstaging and oncological outcomes

in clinical stage T2 bladder cancer. BJU Int 2013;111:240–8.

[39] Ahmadi H, Mitra AP, Abdelsayed GA, et al. Principal component

analysis based pre-cystectomy model to predict pathological stage

in patients with clinical organ-confined bladder cancer. BJU Int

2013;111:E167–72.

[40] Shariat SF, Palapattu GS, Karakiewicz PI, et al. Discrepancy be-

tween clinical and pathologic stage: impact on prognosis after

radical cystectomy. Eur Urol 2007;51:137–49, discussion 149–51.

[41] Grossman HB, Natale RB, Tangen CM, et al. Neoadjuvant chemo-

therapy plus cystectomy compared with cystectomy alone for

locally advanced bladder cancer. N Engl J Med 2003;349:859–66.

[42] Griffiths G, Hall R, Sylvester R, et al. International phase III trial

assessing neoadjuvant cisplatin, methotrexate, and vinblastine

chemotherapy for muscle-invasive bladder cancer: long-term

results of the BA06 30894 trial. J Clin Oncol 2011;29:2171–7.

[43] Advanced Bladder Cancer (ABC) Meta-analysis Collaboration.

Neoadjuvant chemotherapy in invasive bladder cancer: update

of a systematic review and meta-analysis of individual patient

data. Eur Urol 2005;48:202–5, discussion 205–6.

[44] Takata R, Katagiri T, Kanehira M, et al. Predicting response to

methotrexate, vinblastine, doxorubicin, and cisplatin neoadjuvant

chemotherapy for bladder cancers through genome-wide gene

expression profiling. Clin Cancer Res 2005;11:2625–36.

[45] Takata R, Obara W, Fujioka T. Study of the prediction system for

clinical response to M-VAC neoadjuvant chemotherapy for blad-

der cancer. Aktuelle Urol 2010;41(Suppl 1):S41–5.

[46] Smith SC, Baras AS, Dancik G, et al. A 20-gene model for molecular

nodal staging of bladder cancer: development and prospective

assessment. Lancet Oncol 2011;12:137–43.

[47] Shariat SF, Ehdaie B, Rink M, et al. Clinical nodal staging scores for

bladder cancer: a proposal for preoperative risk assessment. Eur

Urol 2012;61:237–42.

[48] Shariat SF, Rink M, Ehdaie B, et al. Pathologic nodal staging score

for bladder cancer: a decision tool for adjuvant therapy after

radical cystectomy. Eur Urol 2013;63:371–8.

[49] Karakiewicz PI, Shariat SF, Palapattu GS, et al. Nomogram for

predicting disease recurrence after radical cystectomy for transi-

tional cell carcinoma of the bladder. J Urol 2006;176:1354–61,

discussion 1361–2.

[50] Shariat SF, Karakiewicz PI, Palapattu GS, et al. Nomograms provide

improved accuracy for predicting survival after radical cystec-

tomy. Clin Cancer Res 2006;12:6663–76.

[51] Capitanio U, Suardi N, Shariat SF, et al. Assessing the minimum

number of lymph nodes needed at radical cystectomy in patients

with bladder cancer. BJU Int 2009;103:1359–62.

[52] Dancik G, Aisner D, Theodorescu D. A 20 gene model for predicting

nodal involvement in bladder cancer patients with muscle inva-

sive tumors. PLoS Curr 2011;3:RRN1248.

[53] Leissner J, Ghoneim MA, Abol-Enein H, et al. Extended radical

lymphadenectomy in patients with urothelial bladder cancer:

results of a prospective multicenter study. J Urol 2004;171: 139–44.

[54] May M, Herrmann E, Bolenz C, et al. Association between the

number of dissected lymph nodes during pelvic lymphadenecto-

my and cancer-specific survival in patients with lymph node-

negative urothelial carcinoma of the bladder undergoing radical

cystectomy. Ann Surg Oncol 2011;18:2018–25.

[55] Isbarn H, Jeldres C, Zini L, et al. A population based assessment of

perioperative mortality after cystectomy for bladder cancer. J Urol

2009;182:70–7.

[56] Morgan TM, Keegan KA, Barocas DA, et al. Predicting the proba-

bility of 90-day survival of elderly patients with bladder cancer

treated with radical cystectomy. J Urol 2011;186:829–34.

[57] Abdollah F, Sun M, Schmitges J, et al. Development and validation

of a reference table for prediction of postoperative mortality rate

in patients treated with radical cystectomy: a population-based

study. Ann Surg Oncol 2012;19:309–17.

[58] Solsona E, Iborra I, Dumont R, et al. Risk groups in patients with

bladder cancer treated with radical cystectomy: statistical and

clinical model improving homogeneity. J Urol 2005;174:1226–30.

[59] Bochner BH, Kattan MW, Vora KC. Postoperative nomogram pre-

dicting risk of recurrence after radical cystectomy for bladder

cancer. J Clin Oncol 2006;24:3967–72.

[60] Bassi P, Sacco E, De Marco V, et al. Prognostic accuracy of an

artificial neural network in patients undergoing radical cystec-

tomy for bladder cancer: a comparison with logistic regression

analysis. BJU Int 2007;99:1007–12.

[61] Shariat SF, Karakiewicz PI, Ashfaq R, et al. Multiple biomarkers

improve prediction of bladder cancer recurrence and mortality in

patients undergoing cystectomy. Cancer 2008;112:315–25.

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3 251

[62] Shariat SF, Bolenz C, Godoy G, et al. Predictive value of combined

immunohistochemical markers in patients with pT1 urothelial

carcinoma at radical cystectomy. J Urol 2009;182:78–84.

[63] Mitra AP, Pagliarulo V, Yang D, et al. Generation of a concise gene

panel for outcome prediction in urinary bladder cancer. J Clin

Oncol 2009;27:3929–37.

[64] Umbreit EC, Crispen PL, Shimko MS, et al. Multifactorial, site-

specific recurrence model after radical cystectomy for urothelial

carcinoma. Cancer 2010;116:3399–407.

[65] Shariat SF, Chade DC, Karakiewicz PI, et al. Combination of multi-

ple molecular markers can improve prognostication in patients

with locally advanced and lymph node positive bladder cancer.

J Urol 2010;183:68–75.

[66] Sonpavde G, Khan MM, Svatek RS, et al. Prognostic risk stratifica-

tion of pathological stage T2N0 bladder cancer after radical cys-

tectomy. BJU Int 2011;108:687–92.

[67] Sonpavde G, Khan MM, Svatek RS, et al. Prognostic risk stratifica-

tion of pathological stage T3N0 bladder cancer after radical cys-

tectomy. J Urol 2011;185:1216–21.

[68] Kim Y, Kim TK, Kim Y, et al. Principal network analysis: identifi-

cation of subnetworks representing major dynamics using gene

expression data. Bioinformatics 2011;27:391–8.

[69] Gakis G, Todenhofer T, Renninger M, et al. 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 2011;108:1800–5.

[70] Todenhofer T, Renninger M, Schwentner C, et al. A new prognostic

model for cancer-specific survival after radical cystectomy includ-

ing pretreatment thrombocytosis and standard pathological risk

factors. BJU Int 2012;110:E533–40.

[71] Koga F, Fujii Y, Masuda H, et al. Pathology-based risk stratification

of muscle-invasive bladder cancer patients undergoing cystec-

tomy for persistent disease after induction chemoradiotherapy in

bladder-sparing approaches. BJU Int 2012;110:E203–8.

[72] Gondo T, Nakashima J, Ozu C, et al. Risk stratification of survival by

lymphovascular invasion, pathological stage, and surgical margin

in patients with bladder cancer treated with radical cystectomy.

Int J Clin Oncol 2012;17:456–61.

[73] Ishioka J, Saito K, Sakura M, et al. Development of a nomogram

incorporating serum C-reactive protein level to predict overall

survival of patients with advanced urothelial carcinoma and

its evaluation by decision curve analysis. Br J Cancer 2012;107:

1031–6.

[74] Riester M, Taylor JM, Feifer A, et al. Combination of a novel gene

expression signature with a clinical nomogram improves the

prediction of survival in high-risk bladder cancer. Clin Cancer

Res 2012;18:1323–33.

[75] Shariat SF, Chromecki TF, Cha EK, et al. Risk stratification of organ

confined bladder cancer after radical cystectomy using cell cycle

related biomarkers. J Urol 2012;187:457–62.

[76] Xylinas E, Cha EK, Sun M, et al. Risk stratification of pT1-3N0

patients after radical cystectomy for adjuvant chemotherapy

counselling. Br J Cancer 2012;107:1826–32.

[77] Rink M, Hansen J, Cha EK, et al. Outcomes and prognostic factors in

patients with a single lymph node metastasis at time of radical

cystectomy. BJU Int 2013;111:74–84.

[78] Buchner A, May M, Burger M, et al. Prediction of outcome in

patients with urothelial carcinoma of the bladder following radi-

cal cystectomy using artificial neural networks. Eur J Surg Oncol

2013;39:372–9.

[79] Lotan Y, Bagrodia A, Passoni N, et al. Prospective evaluation of a

molecular marker panel for prediction of recurrence and cancer-

specific survival after radical cystectomy. Eur Urol 2013;64:

465–71.

[80] Eisenberg MS, Boorjian SA, Cheville JC, et al. The SPARC score: a

multifactorial outcome prediction model for patients undergoing

radical cystectomy for bladder cancer. J Urol 2013;190:2005–10.

[81] Baumann BC, Guzzo TJ, He J, et al. A novel risk stratification to

predict local-regional failures in urothelial carcinoma of the blad-

der after radical cystectomy. Int J Radiat Oncol Biol Phys 2013;85:

81–8.

[82] Sejima T, Morizane S, Yao A, et al. Prognostic impact of preopera-

tive hematological disorders and a risk stratification model in

bladder cancer patients treated with radical cystectomy. Int J Urol

2014;21:52–7.

[83] Vickers AJ, Elkin EB. Decision curve analysis: a novel method for

evaluating prediction models. Med Decis Making 2006;26:565–74.

[84] Vickers AJ, Cronin AM, Kattan MW, et al. Clinical benefits of a

multivariate prediction model for bladder cancer: a decision

analytic approach. Cancer 2009;115:5460–9.

[85] Ploussard G, Shariat SF, Dragomir A, et al. Conditional survival

after radical cystectomy for bladder cancer: evidence for a patient

changing risk profile over time. Eur Urol 2014;66:361–70.

[86] Karam JA, Lotan Y, Karakiewicz PI, et al. Use of combined apoptosis

biomarkers for prediction of bladder cancer recurrence and mor-

tality after radical cystectomy. Lancet Oncol 2007;8:128–36.

[87] Wang LC, Xylinas E, Kent MT, et al. Combining smoking informa-

tion and molecular markers improves prognostication in patients

with urothelial carcinoma of the bladder. Urol Oncol 2014;32:

433–40.

[88] Stenzl A, Cowan NC, De Santis M, et al. Treatment of muscle-

invasive and metastatic bladder cancer: update of the EAU guide-

lines. Eur Urol 2011;59:1009–18.

[89] Stein JP, Lieskovsky G, Cote R, et al. Radical cystectomy in the

treatment of invasive bladder cancer: long-term results in 1,054

patients. J Clin Oncol 2001;19:666–75.

[90] Shariat SF, Karakiewicz PI, Palapattu GS, et al. Outcomes of radical

cystectomy for transitional cell carcinoma of the bladder: a con-

temporary series from the Bladder Cancer Research Consortium.

J Urol 2006;176:2414–22, discussion 2422.

[91] Madersbacher S, Hochreiter W, Burkhard F, et al. Radical cystec-

tomy for bladder cancer today–a homogeneous series without

neoadjuvant therapy. J Clin Oncol 2003;21:690–6.

[92] Bajorin DF, Dodd PM, Mazumdar M, et al. Long-term survival in

metastatic transitional-cell carcinoma and prognostic factors pre-

dicting outcome of therapy. J Clin Oncol 1999;17:3173–81.

[93] Bellmunt J, von der Maase H, Mead GM, et al. Randomized phase III

study comparing paclitaxel/cisplatin/gemcitabine and gemcita-

bine/cisplatin in patients with locally advanced or metastatic

urothelial cancer without prior systemic therapy: EORTC Inter-

group Study 30987. J Clin Oncol 2012;30:1107–13.

[94] Mayr R, May M, Martini T, 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;62:662–70.

[95] Nakagawa T, Hara T, Kawahara T, et al. Prognostic risk strati-

fication of patients with urothelial carcinoma of the bladder

with recurrence after radical cystectomy. J Urol 2013;189:

1275–81.

[96] Ploeg M, Kums AC, Aben KK, et al. Prognostic factors for survival in

patients with recurrence of muscle invasive bladder cancer after

treatment with curative intent. Clin Genitourin Cancer 2011;9:

14–21.

[97] De Santis M, Bellmunt J, Mead G, et al. Randomized phase II/III trial

assessing gemcitabine/carboplatin and methotrexate/carboplatin/

vinblastine in patients with advanced urothelial cancer ‘‘unfit’’ for

cisplatin-based chemotherapy: phase II–results of EORTC study

30986. J Clin Oncol 2009;27:5634–9.

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3252

[98] Apolo AB, Ostrovnaya I, Halabi S, et al. Prognostic model for

predicting survival of patients with metastatic urothelial cancer

treated with cisplatin-based chemotherapy. J Natl Cancer Inst

2013;105:499–503.

[99] Gibbs J, Cull W, Henderson W, et al. Preoperative serum albumin

level as a predictor of operative mortality and morbidity: results

from the National VA Surgical Risk Study. Arch Surg 1999;134:

36–42.

[100] Gregg JR, Cookson MS, Phillips S, et al. Effect of preoperative

nutritional deficiency on mortality after radical cystectomy for

bladder cancer. J Urol 2011;185:90–6.

[101] Bellmunt J, Choueiri TK, Fougeray R, et al. Prognostic factors in

patients with advanced transitional cell carcinoma of the urothe-

lial tract experiencing treatment failure with platinum-containing

regimens. J Clin Oncol 2010;28:1850–5.

[102] Niegisch G, Fimmers R, Siener R, et al. Prognostic factors in

second-line treatment of urothelial cancers with gemcitabine

and paclitaxel (German Association of Urological Oncology trial

AB20/99). Eur Urol 2011;60:1087–96.

[103] Kluth LA, Xylinas E, Rieken M, et al. Impact of peri-operative blood

transfusion on the outcomes of patients undergoing radical cys-

tectomy for urothelial carcinoma of the bladder. BJU Int 2014;113:

393–8.

[104] Galsky MD, Moshier E, Krege S, et al. Nomogram for predicting

survival in patients with unresectable and/or metastatic urothe-

lial cancer who are treated with cisplatin-based chemotherapy.

Cancer 2013;119:3012–9.

[105] Green DA, Rink M, Xylinas E, et al. Urothelial carcinoma of the

bladder and the upper tract: disparate twins. J Urol 2013;189:

1214–21.

[106] Kluth LA, Xylinas E, Rieken M, et al. Prognostic model for predict-

ing survival in patients with disease recurrence following radical

cystectomy. Eur Urol Focus. In press. http://dx.doi.org/10.1016/j.

euf.2014.10.003

E U R O P E A N U R O L O G Y 6 8 ( 2 0 1 5 ) 2 3 8 – 2 5 3 253