Open Access

ARTICLE

Prostate cancer nomograms are superior to neural networks

Pierre I. Karakiewicz^1,2, Felix K.-H. Chun^2,3, Alberto Briganti², Paul Perrotte¹, Michael McCormack¹, François Bénard¹, Luc Valiquette¹, Markus Graefen³, Fred Saad¹

1 Department of Urology, University of Montreal, Montreal, Quebec, Canada
2 Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada
3 Department of Urology, University of Hamburg, Germany
4 Department of Urology, University Vita Salute, Milan, Italy
Address correspondence to Dr. Pierre I. Karakiewicz, Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center (CHUM), 1058, rue St-Denis, Montréal, Québec, H2X 3J4 Canada

Canadian Journal of Urology 2006, 13(Suppl.2), 18-25.

Abstract

Introduction: Several nomograms have been developed to predict PCa related outcomes. Neural networks represent an alternative.
Methods: We provide a descriptive and an analytic comparison of nomograms and neural networks, with focus on PCa detection.
Results: Our results indicate that nomograms have several advantages that distinguish them from neural networks. These are both quantitative and qualitative.
Conclusion: In the field of PCa detection, nomograms appear to outweigh the benefits of neural networks. However, the neural network methodology represents a valid alternative, which should not be underestimated.

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