PREOPERATIVE NEURAL NETWORK USING COMBINED MAGNETIC RESONANCE IMAGING VARIABLES, PROSTATE SPECIFIC ANTIGEN AND GLEASON SCORE TO PREDICT PROSTATE CANCER STAGE

Abstract

Purpose: We developed an artificial neural network analysis (ANNA) to predict prostate cancer pathological stage more effectively than logistic regression (LR) based on the combined use of prostate specific antigen (PSA), biopsy Gleason score and pelvic coil magnetic resonance imaging (pMRI) in patients with clinically organ confined disease before radical prostatectomy. Materials and Methods: In 201 consecutive patients undergoing radical retropubic prostatectomy with pelvic lymphadenectomy the radiological-pathological correlation was evaluated using pMRI. Predictive variables were clinical TNM classification, preoperative serum PSA, biopsy Gleason score and pMRI findings. The predicted results were organ confined vs nonorgan confined disease and lymphatic vs no lymphatic involvement. The predicted ability of ANNA with several parameters in a set of 160 randomly selected test data was compared with that of LR and the Partin tables by area under the receiver operating characteristic curve analysis. Results: The overall accuracy of ANNA and LR was 88% and 91%, and 77% and 84% for nonorgan confined and lymphatic involvement, respectively. For nonorgan confined disease and lymph node involvement the area under the curve of ANNA (0.895 and 0.899) was significantly larger than that of LR and the Partin tables (0.722 and 0.751, and 0.750 and 0.733, respectively, p <0.05). Gleason score represented the most influential predictor (relative weight 2.05) of nonorgan confined disease, followed by pMRI findings (1.96), PSA (1.73) and clinical stage (0.89). Conclusions: ANNA is superior to LR for accurately predicting pathological stage. The relative importance of pMRI findings and the usefulness of ANNA for predicting pathological stage in individuals must be confirmed in a prospective trial.