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Artificial neural networks in urology: Update 2000

Prostate Cancer and Prostatic Diseases volume 2, pages 222–226 (1999)Cite this article

Abstract

Artificial neural networks (ANNs) are widely available and have been demonstrated to be superior to standard empirical methods of detecting, staging and monitoring prostate cancer. These algorithms have been statistically validated in diverse, well-characterized patient groups and are now being evaluated for clinical use worldwide. New variables based on demographic data, tissue and serum markers show promise for improving our ability to predict disease extent and outcome and may be integrated in future ANN models. This review focuses on recently developed neural networks for detecting, staging and monitoring prostate cancer.

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Authors and Affiliations

  1. James Buchanan Brady Urological Institute, The Johns Hopkins Hospital, Baltimore, MD

    T Reckwitz, SR Potter & AW Partin

  2. Xaim Inc., Colorado Springs, CO

    PB Snow

  3. Department of Biometry and Epidemiology, Medical University of South Carolina and Horus Therapeutic Inc., Charleston, SC

    Z Zhang

  4. Urosciences Group, UroCor Inc., Oklahoma City, OK

    RW Veltri

Authors
  1. T Reckwitz
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  2. SR Potter
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  3. PB Snow
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  4. Z Zhang
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  5. RW Veltri
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  6. AW Partin
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Reckwitz, T., Potter, S., Snow, P. et al. Artificial neural networks in urology: Update 2000. Prostate Cancer Prostatic Dis 2, 222–226 (1999). https://doi.org/10.1038/sj.pcan.4500374

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  • DOI: https://doi.org/10.1038/sj.pcan.4500374

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