A multivariable logistic regression equation to evaluate prostate cancer

Background/PurposeA 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.MethodsBetween 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.ResultsA 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 < PSA ≤ 10), + 2.02 (when 10 < PSA ≤ 24), + 2.28 (when 24 < PSA ≤ 50), + 3.93 (when 50 < PSA), + 1.23 (when 65 < age ≤ 75), + 1.66 (when 75 < age), followed by ROC curve analysis, we showed that the sensitivity was 88.5% and specificity was 79.1% in predicting the possibility of prostate cancer.ConclusionClinicians can tailor each patient’s follow-up according to the nomogram based on this equation to increase the efficacy of evaluating for prostate cancer.