PURPOSE: Clinical under staging occurs in 40% to 60% of @entity1 who undergo radical prostatectomy for @entity263 . To decrease under staging several methods of predicting pathological stage preoperatively have been developed based on statistical logistic regression analysis and neural networks. To our knowledge none has been validated in our homogeneous regional @entity1 population to date. We created logistic regression and @entity1274 , and implemented and adapted them into our practice. We also compared the 2 methods to determine their value and practicality in daily clinical practice. We present the results of our novel approach for predicting pathological staging of @entity263 . MATERIALS AND METHODS: Between 1986 and 1999, 600 white @entity1 from the Aragon region of Spain underwent surgery for @entity263 ; of whom 468 were selected for study. Predictive study variables included @entity1 age, clinical stage, biopsy Gleason score and preoperative @entity1486 ( @entity1486 ). The predicted result included in analysis was organ confined or nonorgan confined disease. Data were analyzed by multivariate logistic regression and a supervised neural network (multilayer perceptron and radial basis function). Results were compared by comparing the areas under the receiver operating characteristics curves. RESULTS: We generated 5 logistic regression models. The model created with clinical staging, Gleason biopsy score and @entity1486 distributed in 5 categories (p <0.001) with an area under the receiver operating characteristics curve of 0.840 proved to be most predictive of pathological stage. Similarly of the 6 @entity1274 evaluated the radial basis function model, which included age, clinical stage, Gleason biopsy score and preoperative @entity1486 distributed in 5 categories with an area under the curve of 0.882, proved the most predictive but not superior to the logistic regression model. The difference in the area under the curves in the 2 chosen models was 0.042 (p = 0.1). CONCLUSIONS: It is possible to generate useful predictive models of organ confined disease using logistic regression or neural networks with high indexes of clinical and statistical validity. However, using these variables neural networks did not prove to be better than logistic regression analysis. Therefore, better predictive variables must be identified, preferably nonlinear characteristics with respect to the probability of organ confined @entity5 , to generate better predictive models using neural networks.

The use of neural networks and logistic regression analysis for predicting pathological stage in XXXX undergoing radical prostatectomy: a population based study.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient', 'men'] @entity263 :: ('MESH:D011471', 'Disease') :: ['prostate adenocarcinoma', 'prostate cancer'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural network models'] @entity1486 :: ('354', 'Gene') :: ['PSA', 'prostate specific antigen']
@entity1 :: (9606,Species) :: ['men']
