A multivariable logistic regression equation to evaluate prostate cancer.

Background/purpose: A possible means of decreasing prostate cancer mortality is through improved early detection. We attempted to create an equation to predict the likelihood of having prostate cancer.

Methods: Between January 2005 and May 2008, patients who received prostate biopsies were retrospective evaluated. The relationship between the possibility of prostate cancer and the following variables were evaluated: age; serum prostate specific antigen (PSA) level, prostate volume, numbers of prostatic biopsies, digital rectal examination (DRE) findings, and the presence of hypoechoic nodule under transrectal ultrasonography.

Results: A multivariate regression model was created to predict the possibility of having prostate cancer, and a receiver-operating characteristic (ROC) curve was drawn based on the predictive scoring equation. Using a predictive equation, P=1/(1-e(-x)), where X=-4.88,+1.11 (if DRE positive),+0.75 (if hypoechoic nodule of prostate present),+1.27 (when 7
Conclusion: Clinicians can tailor each patient's follow-up according to the nomogram based on this equation to increase the efficacy of evaluating for prostate cancer.