Purpose: To investigate whether prebiopsy multi-parametric (mp) MRI can help to improve predictive performance in prostate cancer.Experimental Design: Based on a support vector machine (SVM) analysis, we prospectively modeled clinical data (age, PSA, digital rectal examination, transrectal ultrasound, PSA density, and prostate volume) and mp-MRI findings [Prostate Imaging and Reporting and Data System (PI-RADS) score and tumor-node-metastasis stage] in 985 men to predict the risk of prostate cancer. The new nomogram was validated in 493 patients treated at the same institution. Multivariable Cox regression analyses assessed the association between input variables and risk of prostate cancer, and area under the receiver operating characteristic curve (Az) analyzed the predictive ability.Results: At 5-year follow-up period, 34.3% of patients had systemic progression of prostate cancer. Nomogram (SVM-MRI) predicting 5-year prostate cancer rate trained with clinical and mp-MRI data was accurate and discriminating with an externally validated Az of 0.938, positive predictive value (PPV) of 77.4%, and negative predictive value of 91.5%. The improvement was significant (P < 0.001) compared with the nomogram trained with clinical data. When stratified by PSA, SVM-MRI nomogram had high PPV (93.6%) in patients with PSA > 20 ng/mL, with intermediate to low PPV in PSA 10 to 20 ng/mL (64%), PSA 4 to 10 ng/mL (55.8%), and PSA 0 to 4 ng/mL (29%). PI-RADS score (Cox HR, 2.112; P < 0.001), PSA level (HR, 1.435; P < 0.001), and age (HR, 1.012; P = 0.043) were independent predictors of prostate cancer.Conclusions: Featured with low false positive rate, mp-MRI could be the first investigation of a man with a raised PSA before prostate biopsy. Clin Cancer Res; 23(14); 3692-9. ©2017 AACR.
©2017 American Association for Cancer Research.