Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis

Abstract

Purpose To build a decision tree for patients suspected of having prostate cancer using classification and regression tree (CART) analysis. Patients and Methods Data were uniformly collected on 1,433 referred men with a serum prostate-specific antigen (PSA) levels of ≤ 10 ng/mL who underwent a prostate biopsy. Factors analyzed included demographic, laboratory, and ultrasound data (ie, hypoechoic lesions and PSA density [PSAD]). Twenty percent of the data was randomly selected and reserved for study validation. CART analysis was performed in two steps, initially using PSA and digital rectal examination (DRE) alone and subsequently using the remaining variables. Results CART analysis selected a PSA cutoff of more than 1.55 ng/mL for further work-up, regardless of DRE findings. CART then selected the following subgroups at risk for a positive biopsy: (1) PSAD more than 0.165 ng/mL/cc; (2) PSAD ≤ 0.165 ng/mL/cc and a hypoechoic lesion; (3) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and prostate volume ≤ 44.0 cc; and (4) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and 50.25 cc less than prostate volume ≤ 80.8 cc. In the validation data set, specificity and sensitivity were 31.3% and 96.6%, respectively. Cancers that were missed by the CART were Gleason score 6 or less in 93.4% of cases. Receiver operator characteristic curve analysis showed that CART and logistic regression models had similar accuracy (area under the curve = 0.74 v 0.72, respectively). Conclusion Application of CART analysis to the prostate biopsy decision results in a significant reduction in unnecessary biopsies while retaining a high degree of sensitivity when compared with the standard of performing a biopsy of all patients with an abnormal PSA or DRE.