A novel nomogram to predict the probability of prostate cancer on repeat biopsy

Purpose: We developed a predictive model that incorporates clinical data and prostate specific antigen kinetic from general practice to detect prostate cancer in patients with a previously negative prostate biopsy. Materials and Methods: From January 2001 to January 2007 data on 419 men who underwent repeat prostate biopsy with 12 or more cores were used to develop the nomogram. From February 2007 to June 2007 data on 63 men with the same criteria were used to validate the nomogram. The factors that we evaluated for the risk of a positive repeat prostate biopsy were patient age, digital rectal examination findings, total prostate specific antigen, the free-to-total prostate specific antigen ratio, prostate specific antigen density and slope, and previous high grade prostatic intraepithelial neoplasia. Results: On multivariate logistic regression all factors except age and prostate specific antigen showed significant ability to predict the outcome of 12-core repeat prostate biopsy. In the validation group the AUC of the predicted results from the model was 0.856 (95% CI 0.744-0.931), better than that of prostate specific antigen, the free-to-total prostate specific antigen ratio, and prostate specific antigen density and slope (p <0.05). Conclusions: We successfully developed an accurate model to predict the outcome of repeat prostate biopsy. Adding the free-to-total prostate specific antigen ratio, digital rectal examination, prostate specific antigen and slope, and history of high grade prostatic intraepithelial neoplasia sharply improves the accuracy of our model.