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Artificial neural networks and prostate cancer—tools for diagnosis and management

Nature Reviews Urology volume 10, pages 174–182 (2013)Cite this article

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Abstract

Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

Key Points

  • The use of multivariate mathematical models, including artificial neural networks (ANNs), in clinical medicine has increased markedly in the past decade

  • The majority of prostate cancer ANNs are used for diagnosis and staging, providing useful clinical decision support mechanisms to physicians

  • These models have greater specificity than serum PSA and percent free PSA as sole predictors in the diagnosis of prostate cancer

  • The number of ANNs that can be used for prognosis, recurrence risk and prediction of metastasis is limited

  • For the staging of prostate cancer, Bayesian neural networks might be superior to other ANNs and bivariate logistic regression models

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Figure 1: Basic structure of a feed-forward ANN with error backpropagation for the diagnosis of prostate cancer.

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Acknowledgements

The authors are supported by the Berliner Sparkassenstiftung Medizin and the Wilhelm Sander-Stiftung (grant 2010.111.1).

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  1. Department of Urology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10098, Germany

    Xinhai Hu, Hellmuth-A. Meyer, Kurt Miller & Carsten Stephan

  2. Institute of Medical Informatics, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10098, Berlin, Germany

    Henning Cammann

  3. Berlin Institute for Urologic Research, Robert-Koch Platz 7, 10115, Berlin, Germany

    Klaus Jung

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X. Hu, H.-A. Meyer and C. Stephan researched the data for the article. H. Cammann, H.-A. Meyer, K. Jung and C. Stephan contributed to discussions of content. X. Hu, H. Cammann, K. Jung and C. Stephan wrote the manuscript, and X. Hu, K. Miller, K. Jung and C. Stephan edited the manuscript before submission.

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Hu, X., Cammann, H., Meyer, HA. et al. Artificial neural networks and prostate cancer—tools for diagnosis and management. Nat Rev Urol 10, 174–182 (2013). https://doi.org/10.1038/nrurol.2013.9

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