Abstract
Previous studies have reported association of multiple preoperative factors predicting clinically significant prostate cancer with varying results. We assessed the predictive model using a combination of hormone profile, serum biomarkers, and patient characteristics in order to improve the accuracy of risk stratification of patients with prostate cancer. Data on 224 patients from our prostatectomy database were queried. Demographic characteristics, including age, body mass index (BMI), clinical stage, clinical Gleason score (GS) as well as serum biomarkers, such as prostate-specific antigen (PSA), parathyroid hormone (PTH), calcium (Ca), prostate acid phosphatase (PAP), testosterone, and chromogranin A (CgA), were used to build a predictive model of clinically significant prostate cancer using logistic regression methods. We assessed the utility and validity of prediction models using multiple 10-fold cross-validation. Bias-corrected area under the receiver operating characteristics (ROC) curve (bAUC) over 200 runs was reported as the predictive performance of the models. On univariate analyses, covariates most predictive of clinically significant prostate cancer were clinical GS (OR 5.8, 95% CI 3.1-10.8; P < 0.0001; bAUC = 0.635), total PSA (OR 1.1, 95% CI 1.06-1.2; P = 0.0003; bAUC = 0.656), PAP (OR 1.5, 95% CI 1.1-2.1; P = 0.016; bAUC = 0.583), and BMI (OR 1.064, 95% C.I. 0.998, 1.134; P < 0.056; bAUC = 0.575). On multivariate analyses, the most predictive model included the combination of preoperative PSA, prostate weight, clinical GS, BMI and PAP with bAUC 0.771 ([2.5, 97.5] percentiles = [0.76, 0.78]). Our model using preoperative PSA, clinical GS, BMI, PAP, and prostate weight may be a tool to identify individuals with adverse oncologic characteristics and classify patients according to their risk profiles.