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Prostate Cancer Nomograms and How They Measure Up to Neural Networks

  • Chapter
Prostate Biopsy

Part of the book series: Current Clinical Urology ((CCU))

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Abstract

Nomograms and neural networks represent two distinct methodologic approaches toward prediction of prostate cancer outcomes. The authors of this chapter recommend nomograms because of advantages including increased accuracy and graphic display of input variables and their relative importance. Accuracy, level of complexity, performance characteristics, model generalizability, and advantages relative to available alternatives are important considerations in selecting a predictive tool. Benefiting from continued improvement, prostate cancer nomograms have become accepted in clinical practice worldwide.

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Author information

Authors and Affiliations

  1. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, Canada

    Pierre I. Karakiewicz MD

  2. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH

    Michael W. Kattan PhD

Authors
  1. Pierre I. Karakiewicz MD
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  2. Michael W. Kattan PhD
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Editor information

Editors and Affiliations

  1. Department of Regional Urology, Cleveland Clinic Glickman Urological and Kidney Institute, USA

    J. Stephen Jones MD (Chairman, Associate Professor of Surgery (Urology) (Chairman, Associate Professor of Surgery (Urology)

  2. Cleveland Clinic Lerner College of Medicine at CWRU, Cleveland, OH, USA

    J. Stephen Jones MD (Chairman, Associate Professor of Surgery (Urology) (Chairman, Associate Professor of Surgery (Urology)

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© 2008 Humana Press, a part of Springer Science+Business Media, LLC

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Karakiewicz, P.I., Kattan, M.W. (2008). Prostate Cancer Nomograms and How They Measure Up to Neural Networks. In: Jones, J.S. (eds) Prostate Biopsy. Current Clinical Urology. Humana Press. https://doi.org/10.1007/978-1-60327-078-6_8

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  • DOI: https://doi.org/10.1007/978-1-60327-078-6_8

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-790-7

  • Online ISBN: 978-1-60327-078-6

  • eBook Packages: MedicineMedicine (R0)

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