title: Prediction of survival in patients with esophageal carcinoma using artificial neural networks.
abstract: BACKGROUND
  Accurate estimation of outcome in patients with malignant disease is an important component of the clinical decision-making process. To create a comprehensive prognostic model for esophageal carcinoma, artificial neural networks (ANNs) were applied to the analysis of a range of patient-related and tumor-related variables.
  
  
  METHODS
  Clinical and pathologic data were collected from 418 patients with esophageal carcinoma who underwent resection with curative intent. A data base that included 199 variables was constructed. Using ANN-based sensitivity analysis, the optimal combination of variables was determined to allow creation of a survival prediction model. The accuracy (area under the receiver operator characteristic curve [AUR]) of this ANN model subsequently was compared with the accuracy of the conventional statistical technique: linear discriminant analysis (LDA).
  
  
  RESULTS
  The optimal ANN models for predicting outcomes at 1 year and 5 years consisted of 65 variables (AUR = 0.883) and 60 variables (AUR = 0.884), respectively. These filtered, optimal data sets were significantly more accurate (P < 0.0001) than the original data set of 199 variables. The majority of ANN models demonstrated improved accuracy compared with corresponding LDA models for 1-year and 5-year survival predictions. Furthermore, ANN models based on the optimal data set were superior predictors of survival compared with a model based solely on TNM staging criteria (P < 0.0001).
  
  
  CONCLUSIONS
  ANNs can be used to construct a highly accurate prognostic model for patients with esophageal carcinoma. Sensitivity analysis based on ANNs is a powerful tool for seeking optimal data sets.
authors.affiliation: Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
authors.forename: Fumiaki
authors.initials: F
authors.lastname: Sato
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Shimada
authors.forename: Florin M
authors.initials: FM
authors.lastname: Selaru
authors.forename: David
authors.initials: D
authors.lastname: Shibata
authors.forename: Masato
authors.initials: M
authors.lastname: Maeda
authors.forename: Go
authors.initials: G
authors.lastname: Watanabe
authors.forename: Yuriko
authors.initials: Y
authors.lastname: Mori
authors.forename: Sanford A
authors.initials: SA
authors.lastname: Stass
authors.forename: Masayuki
authors.initials: M
authors.lastname: Imamura
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Meltzer
mesh_terms: D003936:Diagnosis, Computer-Assisted; D004938:Esophageal Neoplasms / Q000401:mortality* / Q000473:pathology / Q000556:secondary; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015996:Survival Rate