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. © 2009 American Cancer Society.

Techniques and predictive models to improve prostate cancer detection.

/pdf/prostate-cancer-5-diagnostic-tools-for-early-detection-47r9zkz510.pdf

Prostate cancer: 5. Diagnostic tools for early detection

OBJECTIVE: To reassess PSA density (PSAD) in the detection of non-palpable, isoechoic prostate cancer and to analyze the effect of potentially inaccurate non-planimetric transrectal ultrasound volume estimates on the diagnostic performance of this diagnostic tool. METHODS: We prospectively evaluated 343 consecutive men with non-suspicious digital rectal examination and transrectal ultrasound findings and with serum PSA in the intermediate range (4.1-10 ng/mL). All men underwent systematic sextant biopsies of the peripheral zone. We performed a two-fold analysis of PSAD performance first using measured gland volume and then using modified gland volumes ranging from a 25% underestimation to a 25% overestimation, in 5% stepwise increments. RESULTS: With a 0.15 PSAD cut-off, we would have missed 14, 34 and 48% of cancers, if we had respectively used a volume underestimate of 25%, the measured volume and a volume overestimate of 25%. CONCLUSIONS: Potential gland volume under- or overestimation may substantially affect the diagnostic performance of PSAD. Although PSAD may represent a useful adjunct in the early detection of prostate cancer, it may compromise cancer detection in a substantial proportion of cases, if used as the only indicator for biopsy.

Analysis of gland volume effect on the performance of PSA density in early detection of non-palpable, isoechoic prostate cancer.

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Analysis of gland volume effect on the performance of PSA density in early detection of non-palpable, isoechoic prostate cancer.