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Predicting Outcomes

Artificial Neural Networks and Nomograms

  • Chapter
Book cover Management of Prostate Cancer

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

  • 179 Accesses

Abstract

In 2002, an estimated 189,000 new cases of prostate cancer were diagnosed, with approx 30,200 deaths related to diseases (1). Thus prostate cancer is the most common cancer and the second most common cause of cancer death in American men. The advent of prostate-specific antigen (PSA) screening has led to a stage migration, whereby most prostate cancers are discovered while the tumor is still confined to the gland proper (2). Treatments for clinically localized tumors include radical prostatectomy (RP) (3–6), external-beam radiotherapy (XRT) (7,8) brachytherapy (9), and conservative management, with or without androgen-deprivation therapy (ADT) (10,11). Although randomized clinical trials are under way to compare treatment options (12), it is uncertain which treatment provides the best outcomes with respect to cancer control, as well as quality of life. The natural history of prostate cancer is generally indolent, requiring many years to elapse between its diagnosis and the development of disease-related symptoms. Therefore, a patient with prostate cancer is more likely to be affected by competing risk factors (i.e., comorbid illness) during his lifetime, and thus there is an obvious need for instruments to aid the patient and his physician in decision making when selecting a therapy for prostate cancer.

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Authors
  1. Audrey C. Rhee
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  2. Christopher J. Di Blasio
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  3. Daniel Cho
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  4. Michael W. Kattan
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Editors and Affiliations

  1. Section of Urology Oncology, Glickman Urological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA

    Eric A. Klein MD (Head) (Head)

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Rhee, A.C., Di Blasio, C.J., Cho, D., Kattan, M.W. (2004). Predicting Outcomes. In: Klein, E.A. (eds) Management of Prostate Cancer. Current Clinical Urology. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-59259-776-5_9

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