Literature DB >> 15066233

Artificial neural networks for predictive modeling in prostate cancer.

Eduard J Gamito1, E David Crawford.   

Abstract

Artificial neural networks (ANNs) represent a relatively new methodology for predictive modeling in medicine. ANNs, a form of artificial intelligence loosely based on the brain, have a demonstrated ability to learn complex and subtle relationships between variables in medical applications. In contrast with traditional statistical techniques, ANNs are capable of automatically resolving these relationships without the need for a priori assumptions about the nature of the interactions between variables. As with any technique, ANNs have limitations and potential drawbacks. This article provides an overview of the theoretical basis of ANNs, how they function, their strengths and limitations, and examples of how ANNs have been used to develop predictive models for the management of prostate cancer.

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Year:  2004        PMID: 15066233     DOI: 10.1007/s11912-004-0052-z

Source DB:  PubMed          Journal:  Curr Oncol Rep        ISSN: 1523-3790            Impact factor:   5.075


  27 in total

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Journal:  Urology       Date:  1999-12       Impact factor: 2.649

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Journal:  J Urol       Date:  2003-05       Impact factor: 7.450

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Journal:  Cancer       Date:  2001-04-15       Impact factor: 6.860

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Journal:  J Natl Cancer Inst       Date:  1998-05-20       Impact factor: 13.506

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Journal:  Radiology       Date:  1983-09       Impact factor: 11.105

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Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

9.  An artificial neural network to predict the outcome of repeat prostate biopsies.

Authors:  Mesut Remzi; Theodore Anagnostou; Vincent Ravery; Alexandre Zlotta; Carsten Stephan; Michael Marberger; Bob Djavan
Journal:  Urology       Date:  2003-09       Impact factor: 2.649

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Authors:  P H Gann; C H Hennekens; M J Stampfer
Journal:  JAMA       Date:  1995-01-25       Impact factor: 56.272

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  2 in total

1.  Development of a method based on surface enhanced laser desorption and ionization time of flight mass spectrometry for rapid identification of Klebsiella pneumoniae.

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Journal:  J Microbiol       Date:  2009-10-24       Impact factor: 3.422

2.  Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study.

Authors:  Kathrine Røe; Manish Kakar; Therese Seierstad; Anne H Ree; Dag R Olsen
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  2 in total

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