A neural network predicts progression for men with Gleason score 3+4 versus 4+3 tumors after radical prostatectomy

Objectives. To determine the significance of Gleason scores 3+4 (GS3+4) versus 4+3 (GS4+3) with respect to biochemical recurrence in a retrospective review of a series of men with clinically localized prostate cancer who underwent radical retropubic prostatectomy (RRP) and to develop and test an artificial neural network (ANN) to predict the biochemical recurrence after surgery for this group of men using the pathologic and clinical data. Methods. From 1982 to 1998, 600 men had pathologic Gleason score 7 disease without lymph node or seminal vesicle involvement. We analyzed the freedom from biochemical (prostate-specific antigen) progression after RRP on 564 of these men on the basis of their GS3+4 versus GS4+3 (Gleason 7) status. The Cox proportional hazards model was used to determine the importance of Gleason 7 status as an independent predictor of progression. In addition, an ANN was developed using randomly selected training and validation sets for predicting biochemical recurrence at 3 or 5 years. Different input variable subsets, with or without Gleason 7 status, were compared for the ability of the ANN to maximize the prediction of progression. Standard logistic regression was used concurrently on the same random patient population sets to calculate progression risk. Results. A significant recurrence-free survival advantage was found in men who underwent RRP for GS3+4 compared with those with GS4+3 disease (P < 0.0001). The ANN, logistic regression, and proportion hazard models demonstrated the importance of Gleason 7 status in predicting patient outcome. The ANN was better than logistic regression in predicting patient outcome, in terms of prostate-specific antigen progression, at 3 and 5 years. Conclusions. A simple modification of the Gleason scoring system for men with Gleason 7 disease revealed a difference in the patient outcome after RRP. ANN models can be developed and used to better predict patient outcome when pathologic and clinical features are known. (C) 2000, Elsevier Science Inc.