Objectives: To identify the predictive variables affecting the outcome after radical surgery for bladder cancer by a newer statistical methodology, i.e. nonparametric combination (NPC). Methods: A multicenter study enrolled 1,312 patients who had undergone radical cystectomy for bladder cancer in 11 Italian oncological centers from January 1982 to December 2002. A statistical analysis of their medical history and diagnostic, pathological and postoperative variables was performed using a NPC test. The patients were included in a comprehensive database with medical history and clinical and pathological data. Five-year survival was used as the dependent variable, and p values were corrected for multiplicity using a closed testing procedure. The newer nonparametric approach was used to evaluate the prognostic importance of the variables. All of the analyses were performed using routines developed in MATLAB© and the significance level was set at α = 0.05. Results: A significant prognostic predictive value (p < 0.01) for tumor clinical staging, hydronephrosis, tumor pathological staging, grading, presence of concomitant carcinoma in situ, regional lymph node involvement, corpora cavernosa invasion, microvascular invasion, lymphatic invasion and prostatic stroma involvement was found. Conclusions: The NPC test could handle any type of variable (categorical and quantitative) and take into account the multivariate relation among variables. This newer methodology offers a significant contribution in biomedical studies with several endpoints and is recommended in presence of non-normal data and missing values, as well as solving high-dimensional data and problems relating to small sample sizes.

Keywords:
Bladder cancer, Prognosis, Permutation test
1.
Jemal A, Siegel R, Ward E, et al: Cancer statistics, 2008. CA Cancer J Clin 2008;58:71-96.
2.
Herr HW, Faulkner JR, Grossman HB, et al: Surgical factors influence bladder cancer outcomes: a cooperative group report. J Clin Oncol 2004;22:2781-2789.
3.
Bassi P, Ferrante GD, Piazza N, et al: Prognostic factors of outcome after radical cystectomy for bladder cancer: a retrospective study of a homogeneous patients cohort. J Urol 1999;161:1494-1497.
4.
Dalbagni G, Genega E, Hashibe M, et al: Cystectomy for bladder cancer: a contemporary series. J Urol 2001;165:1111-1116.
5.
Madersbacher S, Hochreiter W, Burkhard F, et al: Radical cystectomy for bladder cancer today - a homogeneous series without neoadjuvant therapy. J Clin Oncol 2003;21:690-696.
6.
Monzó Gardiner JI, Herranz Amo F, Díez Cordero JM, et al: Factores pronósticos en la supervivencia de los pacientes con carcinoma transicional de vejiga tratados con cistectomía radical. Actas Urol Esp 2009;33:249-257.
7.
Shabsigh A, Bochner BH: Use of nomograms as predictive tools in bladder cancer. World J Urol 2006;24:489-498.
8.
Finos L, Salmaso L: Weighted methods controlling the multiplicity when the number of variables is much higher than the number of observations. J Nonparametr Stat 2006;18:245-261.
9.
Sobin LH, Greene FL: TNM classification: clarification of number of regional lymph nodes for pNo. Cancer 2001;92:452.
10.
Epstein JI, Amin MB, Reuter VR, et al: The World Health Organization/International Society of Urological Pathology consensus classification of urothelial (transitional cell) neoplasms of the urinary bladder. Bladder Consensus Conference Committee. Am J Surg Pathol 1998;22:1435-1448.
11.
Pesarin F, Salmaso L: Permutation Tests for Complex Data. Hoboken, John Wiley & Sons, 2010.
12.
Beahrs OH: How can general surgery survive? Am Surg 1992;58:17-21.
13.
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;9:4172-4177.
14.
Burke HB, Goodman PH, Rosen DB, et al: Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997;79:857-862.
15.
Frazier HA, Robertson JE, Dodge RK, Paulson DF: The value of pathologic factors in predicting cancer-specific survival among patients treated with radical cystectomy for transitional cell carcinoma of the bladder and prostate. Cancer 1993;71:3993-4001.
16.
Bassi P, Sacco E, De Marco V, et al: Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. BJU Int 2007;99:1007-1012.
17.
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;174:1226-1230.
18.
Qureshi KN, Naguib RN, Hamdy FC, et al: Neural network analysis of clinicopathological and molecular markers in bladder cancer. J Urol 2000;163:630-633.
19.
Schwarzer G, Vach W, Schumacher M: On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000;19:541-561.
20.
Pesarin F: Extending permutation conditional inference to unconditional one. Stat Methods Appt 2002;11:161-173.
© 2014 S. Karger AG, Basel
2014
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