Preoperative Neural Network Using Combined Magnetic Resonance Imaging Variables, Prostate Specific Antigen, and Gleason Score to Predict Prostate Cancer Recurrence after Radical Prostatectomy

An artificial neural network analysis (ANNA) was developed to predict the biochemical recurrence more effectively than regression models based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate specific antigen (PSA) and biopsy Gleason score in patients with clinically organ-confined prostate cancer after radical prostatectomy (RP). Two-hundred-and-ten patients undergoing retropubic RP with pelvic lymphadenectomy were evaluated. Predictive study variables included clinical TNM classification, preoperative serum PSA, biopsy Gleason score, transrectal ultrasound (TRUS) findings, and pMRI findings. The predicted result was a biochemical failure (PSA ≥0.1 ng/ml). Using a five-way cross-validation method, the predicted ability of ANNA for a validation set of 200 randomly selected patients was compared with those of Cox regression analysis and “Kattan nomogram” by area under the receiver operating characteristic curve (AUC) analysis. Seventy-three patients (35%) failed at median follow-up of 61 (mean: 60, range: 2–94) months. Using similar input variables, the AUC of ANNA (0.765, 95% Confidence Interval [CI]: 0.704–0.825) was comparable (p > 0.05) to those for Cox regression (0.738, 95%CI: 0.691–0.819) and Kattan nomogram (0.728, 95%CI: 0.644–0.819). Contrarily, adding the pMRI findings, the ANNA is significantly (p < 0.05) superior to any other predictive model (0.897, 95%CI: 0.841–0.977). The Gleason score represented the most influential predictor (relative weight: 2.4) of PSA recurrence, followed by pMRI (2.2), and PSA (2.0). ANNA is superior to regression models to predict accurately biochemical recurrence. The relative importance of pMRI and the utility of ANNA to predict the PSA failure in patients referred for RP must be confirmed in further trials.