Techniques and predictive models to improve prostate cancer detection.
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. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.
Herman
Michael P
MP
Department of Urology, New York Presbyterian Hospital-Weill Cornell Medical Center, New York, New York, USA.
Dorsey
Philip
P
John
Majnu
M
Patel
Nishant
N
Leung
Robert
R
Tewari
Ashutosh
A
D001499:Bayes Theorem; D005544:Forecasting; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D049451:Nomograms; D017430:Prostate-Specific Antigen / Q000032:analysis*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D018570:Risk Assessment
D017430:Prostate-Specific Antigen