- Christopher R Porter: Department of Urology, State University of New York, Stony Brook, New York, USA.
OBJECTIVES: To develop a mathematical model to predict prostate biopsy outcome using readily available clinical variables.
METHODS: A total of 319 men (78% African American) undergoing transrectal ultrasound-guided prostate biopsy were prospectively studied. The parameters collected included age, race, prostate-specific antigen (PSA) level, PSA density (PSAD), digital rectal examination findings, biopsy history, prostate volume (by transrectal ultrasound), and ultrasound findings. Models were constructed using multivariate logistic regression (LR) analysis and back-propagation artificial neural networks (ANNs). Patient data were randomly split into five cross-validation sets and used to develop and validate the LR and ANN models.
RESULTS: Of the 319 men, 39% had a positive biopsy. The mean patient age was 65.1 +/- 8.3 years, with a mean PSA level of 12.6 +/- 24.9 ng/mL and a mean PSAD of 0.31 +/- 0.66 ng/mL/cm(3). Univariate analysis indicated a significant difference in age, PSA level, PSAD, free PSA, digital rectal examination findings, TRUS lesion, and biopsy history between the positive and negative biopsy groups (P <0.01). The mean area under the receiver operating characteristic curve (AUROC) for the five LR models was 0.76 +/- 0.04 (range 0.71 to 0.81). The median LR AUROC was 0.76, with a corresponding specificity of 0.13 at a sensitivity of 0.95. The mean AUROC for the five ANN models was 0.76 +/- 0.04 (range 0.71 to 0.83). The median ANN AUROC was 0.76, with a corresponding specificity of 0.21 at a sensitivity of 0.95.
CONCLUSIONS: Two models (LR and ANN) that predict outcome with high efficiency (AUROC = 0.76) were constructed from a contemporary, prospective database. Such models may be useful to patients and physicians alike when assessing the diagnostic strategies available to detect prostate cancer.