@Article{,
AUTHOR = {Pierre I. Karakiewicz, Felix K.-H. Chun, Alberto Briganti, Paul Perrotte, Michael McCormack, François Bénard, Luc Valiquette, Markus Graefen, Fred Saad},
TITLE = {Prostate cancer nomograms are superior to neural networks},
JOURNAL = {Canadian Journal of Urology},
VOLUME = {13},
YEAR = {2006},
NUMBER = {Suppl.2},
PAGES = {18--25},
URL = {http://www.techscience.com/CJU/v13nSuppl.2/63349},
ISSN = {1488-5581},
ABSTRACT = {<b>Introduction:</b> Several nomograms have been developed to predict PCa related outcomes. Neural networks represent an alternative.<br/>
<b>Methods:</b> We provide a descriptive and an analytic comparison of nomograms and neural networks, with focus on PCa detection.<br/>
<b>Results:</b> Our results indicate that nomograms have several advantages that distinguish them from neural networks. These are both quantitative and qualitative.<br/>
<b>Conclusion:</b> 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.},
DOI = {}
}