A multivariable logistic regression equation to evaluate prostate cancer

Abstract

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<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. 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.

Keywords

• Logistic regression
• men’s health
• probability
• prostate cancer
• risk factor
• score