Originally published as JCO Early Release 10.1200/JCO.2005.05.3884 on July 24 2006

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Postoperative Nomogram Predicting Risk of Recurrence After Radical Cystectomy for Bladder Cancer

International Bladder Cancer Nomogram Consortium

Address reprint requests to Bernard H. Bochner, MD, Department of Urology, Memorial Sloan-Kettering Cancer Center, Kimmel Center for Prostate and Urologic Tumors, 353 E 68th St, New York, NY 10021; e-mail: bochnerb{at}mskcc.org

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
PURPOSE: Radical cystectomy and pelvic lymphadenectomy (PLND) remains the standard treatment for localized and regionally advanced invasive bladder cancers. We have constructed an international bladder cancer database from centers of excellence in the management of bladder cancer consisting of patients treated with radical cystectomy and PLND. The goal of this study was the development of a prognostic outcomes nomogram to predict the 5-year disease recurrence risk after radical cystectomy.

PATIENTS AND METHODS: Institutional radical cystectomy databases containing detailed information on bladder cancer patients were obtained from 12 centers of excellence worldwide. Data were collected on more than 9,000 postoperative patients and combined into a relational database formatted with patient characteristics, pathologic details of the pre- and postcystectomy specimens, and recurrence and survival status. Patients with available information for all selected study criteria were included in the formation of the final prognostic nomogram designed to predict 5-year progression-free probability.

RESULTS: The final nomogram included information on patient age, sex, time from diagnosis to surgery, pathologic tumor stage and grade, tumor histologic subtype, and regional lymph node status. The predictive accuracy of the constructed international nomogram (concordance index, 0.75) was significantly better than standard American Joint Committee on Cancer TNM (concordance index, 0.68; P < .001) or standard pathologic subgroupings (concordance index, 0.62; P < .001).

CONCLUSION: We have developed an international bladder cancer nomogram predicting recurrence risk after radical cystectomy for bladder cancer. The nomogram outperformed prognostic models that use standard pathologic subgroupings and should improve our ability to provide accurate risk assessments to patients after the surgical management of bladder cancer.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Bladder cancer represents a significant worldwide health problem, with 356,370 new cases and 145,000 deaths reported as a global estimate for 2002.¹ Although the majority of new bladder tumors present with disease confined to the superficial layers of the bladder wall, approximately one fourth of patients will either present with or subsequently develop invasive cancers that will require aggressive management. Radical cystectomy (RC) has emerged as the primary treatment for localized or regionally advanced, invasive bladder tumors, and high-risk superficial disease resistant to intravesical treatments. Predicting outcomes (survival and risk of disease recurrence) after RC has been most accurately based on pathologic staging of the primary tumor and regional lymph node status. Several large, contemporary radical cystectomy series have documented risks of recurrence and death based on either TNM staging or pathologic groupings that separate organ-confined, extravesical, or node-positive tumors.^2-8 Although these traditional prognostic groupings have provided useful estimates for recurrence risk and survival outcomes, significant variation within each prognostic group has been observed. The observed variation in outcomes is related to the heterogeneity of tumor biology and patient characteristics within each pathologic subgroup. Alternative prognostic models capable of simultaneous evaluation of multiple relevant variables have been shown to provide improved estimates of outcome that more accurately can be applied to an individual patient.⁹ Providing accurate estimates for the risk of disease progression after RC is critical for improved patient/clinician decision making with regard to the need of adjuvant therapy. In addition, accurate prognostic tools will enhance the ability to interpret and compare clinical trials of bladder cancer treatments by assigning risk estimates for the patient populations studied.

Nomograms are statistical models specifically designed to maximize predictive accuracy. In contrast to predictive models that assign prognosis based on risk groups, nomograms provide prognostic information based on a combination of variables that allow for a more individualized prediction of outcome. Multiple studies have demonstrated the superiority of more complex predictive modeling in providing improved accuracy compared with risk group assignment techniques.¹⁰ The goal of this project was to develop an international postoperative predictive nomogram for patients undergoing radical cystectomy for bladder cancer. To accomplish this, we have assembled one of the largest radical cystectomy databases created from centers of excellence worldwide.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
This was an institutional review board–approved study, with all participating sites providing the necessary institutional data use agreements before initiation of the study. A total of 12 centers of excellence provided data from their institutional databases for analysis (Table 1). To collect data for nomogram development, we designed a minimal data set, specified in a relational database. Simplified sample database files were distributed to all participating centers with the first data request. A computerized databank was generated for data transfer. Table 2 lists the required data points requested from all institutions. Additional variables were requested for exploratory purposes, however, these were not mandatory because it was anticipated that many sites would have limited information available and therefore would not be included in the overall final nomogram.

Institutional data sets spanned many years and in some cases decades. Variation in pathologic staging and histologic grading systems were observed and required resolution before generation of the multi-institutional data set. The 1997 American Joint Committee on Cancer (AJCC) staging system was selected for use within the nomogram and each site was required to reformat pathologic staging fields to comply. Histologic grading information was received in multiple systems and also required conversion to a consensus system before combining site-specific data. Based on the differences in data collection from differing centers and the lack of centralized pathology review of all submitted data, it was determined that a binary system of low versus high grade would provide the most reliable information for input into the nomogram, although it was recognized that using this system would not allow for fine details on the importance of tumor grade to be evaluated.

Although preoperative staging information, including results of radiographic studies and examinations under anesthesia, were requested for consideration of inclusion into the nomogram, nonstandardized protocols for their use and interpretation made it difficult to provide a useable final classification. Therefore, this information was not included in the final nomogram model.

Statistical Methods
Cox proportional hazards regression was used for multivariable analysis. Ordinal and continuous variables were fit using restricted cubic splines to relax the linearity assumptions. No variable selection was performed. This Cox model was the basis for the nomogram.

Nomogram validation comprised two activities. First, discrimination was quantified with the concordance index (CI). On a 0.5 to 1.0 scale identical to that of the area under a receiver operating characteristic curve, the CI provides the probability that, in a randomly selected pair of patients in which one patient dies before the other, the patient who died first had the worse predicted outcome from the nomogram. We used bootstrapping to obtain a relatively unbiased estimate.

Second, calibration was assessed. This was done by grouping patients with respect to their nomogram-predicted probabilities and then comparing the mean of the group with the observed Kaplan-Meier estimate of disease-specific survival. Again, bootstrapping correction was used for this activity. All analyses were performed using S-Plus 2000 Professional software (Statistical Sciences, Seattle, WA) with the Design and Hmisc libraries added.

Discrimination of the nomogram was compared against that of the TNM staging system and standard pathologic groupings by computing 10-fold cross-validated predicted probabilities of the nomogram, to avoid comparison of resubstitution predictions.

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Descriptive Analysis of the Patient Population
Table 1 lists the participating centers and the number of bladder cancer patients provided for evaluation. Overall, data on 9,064 patients were submitted. Transitional cell carcinoma (TCC) was represented in the majority of patients submitted (74%; n = 6,748); squamous cell carcinoma (SCC; n = 1,306) and adenocarcinoma (n = 320) represented 14% and 3.5%, respectively (Table 1). Histologic subtype was based on whether the tumor was considered primary TCC, SCC, or adenocarcinoma. TCC with divergent differentiation containing squamous or adenocarcinoma elements was considered as a TCC. Only pure SCC or adenocarcinoma was classified separately. The percentage of patients with TCC, SCC, and adenocarcinoma included in the final nomogram data set was 74%, 21%, and 5%, respectively.

The median follow-up for the entire cohort was 30 months (range, 2 to 378 months). At 4, 6, and 10 years after radical cystectomy, 4,840, 3,132, and 1,412 patients were available for survival and recurrence evaluation, respectively. Based on consensus of the participating centers, it was determined that given the available data, an outcomes nomogram designed to predict progression-free probability would be most useful for patient/clinician decision making for determining the need for adjuvant treatment. As such, patients who had received preoperative systemic chemotherapy or definitive pelvic radiation therapy for their bladder cancer were excluded from the final nomogram data set. In addition, no patient who received adjuvant chemotherapy was included in the final nomogram data set. Patients who had received prior intravesical therapy were included in the data set.

Nomogram Performance Compared With Standard Prognostic Groupings
The CI for predicting 5-year disease-free survival using pathologic groups (organ confined, extravesical disease, positive lymph node(s) [LN]) for our international cohort was 0.62. The standard Kaplan and Meier curves demonstrating disease-free and overall survival based on pathologic findings, for the entire database cohort, are shown in Figures 1 and 2. In comparison, the bootstrap-corrected CI for our final nomogram (Fig 3) was 0.75, which was significantly superior to either AJCC TNM staging (CI, 0.68; P < .001) or standard pathologic grouping models (CI, 0.62; P < .001). When the nomogram was tested for concordance based on histologic subtype, we found that our final nomogram model more accurately predicted outcome for all histologic subtypes compared with current AJCC staging. The CIs of the nomogram, when applied to TCC, adenocarcinoma, and SCC patients, were 0.74, 0.79, and 0.65, respectively. The final calibration curve for the overall group final nomogram is illustrated in Figure 4.

View larger version (10K): [in this window] [in a new window]   Fig 1. Risk of tumor recurrence after radical cystectomy for overall nomogram patient population.

View larger version (10K): [in this window] [in a new window]   Fig 2. Kaplan-Meier survival analysis of overall nomogram patient database.

View larger version (7K): [in this window] [in a new window]   Fig 3. International bladder cancer postoperative nomogram predicting 5-year risk of recurrence after radical cystectomy (RC). Instructions for physicians: Locate the patient’s sex on the sex axis. Draw a straight line up to the points axis to determine how many points toward recurrence the patient should receive. Repeat this process for each of the remaining axes, drawing a straight line each time to the points axis. Sum the points received for each predictive variable and locate this number on the total points axis. Draw a straight line down from the total points to the 60-month progression-free prediction (PFP) axis for the patient’s specific risk of remaining free from recurrence for 5 years. TCC, transitional cell carcinoma; SCC, squamous cell carcinoma; GX, grade unknown; DxToRC, date of diagnosis to radical cystectomy.

View larger version (8K): [in this window] [in a new window]   Fig 4. Calibration curve of final nomogram. PFP, progression-free probability.

Individual Center Performance Using the Overall Nomogram
Institutional variations in patient management and outcomes were assessed to determine each institution's individual performance within the nomogram. To achieve this evaluation, the nomogram was reconstructed systematically after omitting each institution's data and testing this nomogram on the omitted institution's data. That is, each institution served as a validation or test set for the nomogram that lacked that institution's data. The mean CI for all groups was 0.75. The range of CIs for individual centers was 0.65 to 0.78 when each center was used only as test data for testing nomograms.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
Bladder cancer is a worldwide health risk, with more than 350,000 new cases identified annually.¹ The disease consists of two distinct clinical entities that include a low-risk, superficial variant and a more aggressive invasive phenotype. High-risk bladder cancer, which is composed of invasive tumors as well as high-grade superficial tumors that are resistant to intravesical treatments, is characterized by progressive local invasion, regional lymphatic involvement, and subsequent distant metastases. Localized and/or regionally advanced tumors can be treated effectively with aggressive local therapy, which includes surgery or definitive pelvic radiation with or without systemic chemotherapy. RC with pelvic lymphadenectomy (RC-PLN) has emerged as the most effective and widely used definitive local treatment for invasive bladder cancer. RC-PLN cures most patients with organ-confined disease, the majority of patients with locally advanced but node-negative disease, and a significant minority of patients with regionally advanced tumors. Outcomes after RC-PLN have been documented in many contemporary surgical series that have stratified patients by pathologic tumor characteristics, providing an estimate of future survival or recurrence risk.^{2-5,7,8,13-17} Variability in observed outcome within each pathologic group or pathologic subgrouping is noted, however, and highlights the weakness of using risk groups for prognostication because it forces heterogeneity within each assigned group.

Nomograms are predictive tools that are more complex than simple risk group models and provide a tailored assessment of a given patient's risk. An accurate predictive model provides a clinical tool that can be used by both patients and clinicians in evaluating options after cystectomy. A prognostic tool that provides improved individual risk assessment compared with conventional risk models will facilitate patients' decision making because treatment choice can be influenced strongly by risk prediction. This may play a particularly relevant role in the management of bladder cancer patients, given the data supporting a benefit for perioperative chemotherapy. At present, the option of receiving adjuvant chemotherapy on or off a clinical protocol is a reasonable consideration for bladder cancer patients at high risk of postoperative recurrence (P3, P4, or N+ tumors). A patient faced with a decision to choose adjuvant chemotherapy with its potential adverse effects will base the choice on an understanding of the inherent risk that the tumor has for jeopardizing long-term health and survival.

Nomograms as estimators of risk also have a potential role in clinical trial design and interpretation. Nomograms can be used to stratify patients or patient populations by establishing a risk cutoff point for treatment decision making. Patients above an established risk point could be assigned to therapy therefore a more homogeneous study group is provided for evaluation.¹⁸ Alternatively, study populations in different trials or within different treatment groups can be assessed using the nomogram to define their level of risk (ie, risk of experiencing failure after therapy) to more accurately compare responses between groups or individual studies.

We have developed a nomogram predicting the risk of recurrence at 5 years after RC-PLN. The nomogram is the result of a 2.5-year international effort to develop the largest RC database ever assembled. A range of institutions from differing regions, all recognized centers of excellence for the treatment of bladder cancer, was included to provide a broad input of data in an attempt to diversify the population of patients that would benefit from the nomogram. Individual institutional performance within the nomogram differed, with some demonstrating a higher concordance index than others. The heterogeneity in performance is multifactorial, but in part may be related to institutional differences in tumor staging, histologic assignments, surgical staging techniques, and follow-up protocols. Comparison of our nomogram to other similar published tools constructed for other cancer sites demonstrates a similar ability to predict outcome. Given the large number of patients included in the current analysis and the internal validation schemes used to confirm the performance of the nomogram using each individual centers' data set as a validation set, we are confident that the instrument can be applied broadly to centers worldwide. However, we recognize that the patients included in this study were treated at centers of excellence with significant experience in the management of bladder cancer, and therefore may not as accurately represent the outcomes outside that environment.

During construction of the nomogram model, various LN parameters were tested in our models to evaluate whether more detailed LN data would improve the overall performance of the nomogram. Previously published data demonstrated that by using number of LNs removed, number of positive LNs, or ratios of these positive to total LNs, improved outcome predictions in LN-positive patients could be obtained.^{11,12,14-16,19-21} Although we noted that all nomograms that included more specific LN data outperformed TNM and pathologic subgrouping models, only a limited number of patients had such data available. The concordance indexes using LN density (ratio of number of positive LNs/total number of LNs removed; CI, 0.714) and number of positive LNs (CI, 0.732) seemed superior to standard risk group models; however, using the total data set, LN status (positive or negative) seemed to provide the most accurate information (CI, 0.747). This simply may represent the strength of the analysis performed with more than 9,000 patients compared with a more limited subset of patients with more specific LN data. Prospective data collection from all sites will include more specific LN data, which may provide an improved ability to compare the importance of these more detailed node evaluations.

Our goal was not to deliver a perfect predictive tool but to develop one that predicts better than currently available systems. Although we have constructed an improved prognostic instrument based on a large data set of radical cystectomy patients, this study indicates that there are several limitations. The nomogram developed does not specifically make treatment recommendations. It simply provides a means to assess individual patient risk for disease recurrence after RC-PLN assuming no adjuvant therapy is received. This information provides input into the decision-making process but does not substitute for it. In addition, the performance of the current nomogram still leaves ample room for improvement in its predictive ability. Introducing elements of tumor biology may further enhance the performance of the nomogram and future studies are planned to validate recognized molecular markers of bladder cancer progression (p53, RB, p21, and so on), which will also aid in determining the true prognostic significance of these molecular changes when other relevant clinical factors are considered simultaneously.

Future projects could be improved in a number of ways. The greatest amount of time and resources spent on this project went to exchanging data sets and resolving discrepancies between various institutional databases and variable coding schemes. Standardizing to fewer different systems and a common data exchange format would accelerate the pace of this group's project and other collaborative research projects dramatically.

	Appendix

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
The following are members of the International Bladder Cancer Nomogram Consortium: Director, Bernard H. Bochner, MD; Co-Director, Guido Dalbagni, MD. Statistical Group: Michael W. Kattan, PhD (Director), Paul Fearn (bioinformatics coordinator), Kinjal Vora, Hee Song Seo, Lauren Zoref; Mansura University: Hassan Abol-Enein, Mohamed A. Ghoneim; Memorial Sloan-Kettering Cancer Center: Bernard H. Bochner, Guido Dalbagni, Peter T. Scardino, Dean Bajorin; University of Southern California: Donald G. Skinner, John P. Stein, Gus Miranda; Ulm University: Jürgen E. Gschwend, MD, Bjoern G. Volkmer, MD, Richard E. Hautmann, MD; Vanderbilt University: Sam Chang, Michael Cookson, Joseph A. Smith; University of Bern: George Thalman, Urs E. Studer; University of Michigan: Cheryl T. Lee, James Montie; David Wood; Fundació Puigvert: Juan Palou; Laval University: Yyes Fradet, Louis LaCombe, Pierre Simard; Johns Hopkins Medical Center: Mark P. Schoenberg; Baylor College of Medicine: Seth Lerner, Amnon Vazina; University of Padova Medical School: PierFrancesco Bassi; Keio University: Masaru Murai, Eiji Kikuchi.

	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
The authors indicated no potential conflicts of interest.

	Author Contributions

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 

Conception and design: Bernard H. Bochner, Guido Dalbagni, Dean Bajorin, Michael W. Kattan Administrative support:Paul A. Fearn, Kinjal C. Vora, Hee Song Seo, Lauren Zoref Provision of study materials or patients:Bernard H. Bochner, Guido Dalbagni, Michael W. Kattan, Paul Fearn, Kinjal Vora, Hee Song Seo, Lauren Zoref, Hassan Abol-Enein, Mohamed A. Ghoneim, Peter T. Scardino, Dean Bajorin, Donald G. Skinner, John P. Stein, Gus Miranda, Jürgen E. Gschwend, Bjoern G. Volkmer, Richard E. Hautmann, Sam Chang, Michael Cookson, Joseph A. Smith, George Thalman, Urs E. Studer, Cheryl T. Lee, James Montie, David Wood, Juan Palou, Yyes Fradet, Louis LaCombe, Pierre Simard, Mark P. Schoenberg, Seth Lerner, Amnon Vazina, PierFrancesco Bassi, Masaru Murai, Eiji Kikuchi Collection and assembly of data: Bernard H. Bochner, Guido Dalbagni, Michael W. Kattan, Paul Fearn, Kinjal Vora, Hee Song Seo, Lauren Zoref, Hassan Abol-Enein, Mohamed A. Ghoneim, Peter T. Scardino, Dean Bajorin, Donald G. Skinner, John P. Stein, Gus Miranda, Jürgen E. Gschwend, Bjoern G. Volkmer, Richard E. Hautmann, Sam Chang, Michael Cookson, Joseph A. Smith, George Thalman, Urs E. Studer, Cheryl T. Lee, James Montie, David Wood, Juan Palou, Yyes Fradet, Louis LaCombe, Pierre Simard, Mark P. Schoenberg, Seth Lerner, Amnon Vazina, PierFrancesco Bassi, Masaru Murai, Eiji Kikuchi Data analysis and interpretation: Bernard H. Bochner, Michael W. Kattan, Kinjal C. Vora Manuscript writing: Bernard H. Bochner, Michael W. Kattan, Kinjal C. Vora Final approval of manuscript:Bernard H. Bochner, Guido Dalbagni, Michael W. Kattan, Paul Fearn, Kinjal Vora, Hee Song Seo, Lauren Zoref, Hassan Abol-Enein, Mohamed A. Ghoneim, Peter T. Scardino, Dean Bajorin, Donald G. Skinner, John P. Stein, Gus Miranda, Jürgen E. Gschwend, Bjoern G. Volkmer, Richard E. Hautmann, Sam Chang, Michael Cookson, Joseph A. Smith, George Thalman, Urs E. Studer, Cheryl T. Lee, James Montie, David Wood, Juan Palou, Yyes Fradet, Louis LaCombe, Pierre Simard, Mark P. Schoenberg, Seth Lerner, Amnon Vazina, PierFrancesco Bassi, Masaru Murai, Eiji Kikuchi

 

	NOTES

 
published online ahead of print at www.jco.org on July 24, 2006.

From the International Bladder Cancer Nomogram Consortium. The members and affiliations of the Consortium are listed in the Appendix.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... Author Contributions REFERENCES

 
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Submitted January 5, 2006; accepted April 27, 2006.

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