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Predicting Outcomes

Artificial Neural Networks and Nomograms
  • Audrey C. Rhee
  • Christopher J. Di Blasio
  • Daniel Cho
  • Michael W. Kattan
Chapter
  • 142 Downloads
Part of the Current Clinical Urology book series (CCU)

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.

Keywords

Prostate Cancer Radical Prostatec Localize Prostate Cancer Trad Stat Concordance Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Audrey C. Rhee
  • Christopher J. Di Blasio
  • Daniel Cho
  • Michael W. Kattan

There are no affiliations available

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