Techniques and predictive models to improve prostate cancer detection

Michael P. Herman, Philip Dorsey, Majnu John, Nishant Patel, Robert Leung, Ashutosh Tewari

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools.

Original languageEnglish
Pages (from-to)3085-3099
Number of pages15
JournalCancer
Volume115
Issue numberSUPPL 13
DOIs
StatePublished - 1 Jul 2009
Externally publishedYes

Keywords

  • Artificial neural network
  • Detection
  • Nomogram
  • Predictive modeling
  • Prostate cancer
  • Screening

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