Decision Tree Analysis for Prostate Cancer Prediction in Patients with Serum PSA 10 ng/ml or Less

Abstract

Abstract Serum prostate-specific antigen (PSA) testing increases the number of persons who undergo prostate biopsy. However, the best possible strategy for selecting patients for prostate biopsy has not yet been defined. The aim of this study was to develop a classification and regression tree (CART) decision model that can be used to predict significant prostate cancer (PCa) in the course of prostate biopsy for patients with serum PSA levels of 10 ng/ml or less. The following clinicopathological characteristics of patients who had undergone ultrasound-guided transrectal prostate biopsy were collected: age, PSA, digital rectal examination, volume of the prostate, and PSA density (PSAD). CART analysis was carried out by using all predictors. Different aspects of the predictive performances of the prediction model were assessed. In this retrospective study, significant PCa values were detected in 26 (26.8%) of a total of 97 patients. The CART model had three branching levels based on PSAD as the most decisive variable and age. The model sensitivity was 73.1%, the specificity was 80.3% and the accuracy was 78.3%. Our model showed an area under the receiver operating characteristic curve of 82.6%. The model was well calibrated. In conclusion, CART analysis determined that PSAD was the key parameter for the identification of patients with a minimal risk for positive biopsies. The model showed a good discrimination capacity that surpassed individual predictors. However, before recommending its use in clinical practice, an evaluation of a larger and more complete database is necessary for the prediction of significant PCa.