OBJECTIVE: The choice of therapy for @entity263 should depend on a rational preoperative estimate of @entity5 stage. Artificial @entity1274 were used to predict postoperative staging of @entity263 from sextant biopsies and routinely available preoperative data. METHODS: In group I (97 cases), nonorgan confinement ( @entity5 stage > or =pT3a) was predicted on the basis of age and six histopathological variables from sextant biopsies. In group II (77 cases), nonorgan confinement and extraprostatic organ infiltration ( @entity5 classification > or =pT3b) were predicted from age, four histopathological variables, the preoperative PSA level, and the total prostate volume estimated by preoperative ultrasonography. Learning vector quantization (LVQ) networks were applied for this purpose and compared to multilayer perceptrons (MLP) and @entity1650 ( @entity1650 ). RESULTS: Nonorgan confinement could be predicted correctly in 90% of newly presented cases from sextant biopsy histopathology alone. A similar accuracy of predicting nonorgan confinement (83%) was obtained by combining preoperative biopsy histology with clinical data. Extraprostatic organ infiltration could be predicted correctly in 82%. The best results were obtained by LVQ networks, followed by MLP networks and @entity1650 . CONCLUSION: The @entity86 stage of @entity263 can be estimated with high accuracy, sensitivity and specificity from preoperative routine parameters using artificial @entity1274 , especially LVQ networks. The results suggest that this methodology should be evaluated in a larger prospective study.

Prediction of postoperative @entity263 stage on the basis of systematic biopsies using two types of artificial XXXX .

@entity263 :: ('MESH:D011471', 'Disease') :: ['prostatic cancer'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor'] @entity1650 :: ('MESH:D001308', 'Disease') :: ['LDA', 'linear discriminant analysis'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural networks'] @entity86 :: ('MESH:D019106', 'Disease') :: ['postoperative tumor']
@entity1274 :: (MESH:D006319,Disease) :: ['neural networks']
