title: Prediction of postoperative prostatic cancer stage on the basis of systematic biopsies using two types of artificial neural networks.
abstract: OBJECTIVE
  The choice of therapy for prostatic cancer should depend on a rational preoperative estimate of tumor stage. Artificial neural networks were used to predict postoperative staging of prostatic cancer from sextant biopsies and routinely available preoperative data.
  
  
  METHODS
  In group I (97 cases), nonorgan confinement (tumor 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 (tumor 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 linear discriminant analysis (LDA).
  
  
  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 LDA.
  
  
  CONCLUSIONS
  The postoperative tumor stage of prostatic cancer can be estimated with high accuracy, sensitivity and specificity from preoperative routine parameters using artificial neural networks, especially LVQ networks. The results suggest that this methodology should be evaluated in a larger prospective study.
authors.affiliation: Department of Pathology, University of Ulm, Germany. torsten.mattfeldt@medizin.uni-ulm.de
authors.forename: T
authors.initials: T
authors.lastname: Mattfeldt
authors.forename: H A
authors.initials: HA
authors.lastname: Kestler
authors.forename: R
authors.initials: R
authors.lastname: Hautmann
authors.forename: H W
authors.initials: HW
authors.lastname: Gottfried
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000379:methods; D018450:Disease Progression; D005240:Feasibility Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods*; D016571:Neural Networks, Computer; D010865:Pilot Projects; D011184:Postoperative Period; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery; D012680:Sensitivity and Specificity