title: Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions.
abstract: BACKGROUND
  Artificial neural networks are computer programs that can be used to discover complex relations within data sets. They permit the recognition of patterns in complex biological data sets that cannot be detected with conventional linear statistical analysis. One such complex problem is the prediction of outcome for individual patients treated for colorectal cancer. Predictions of outcome in such patients have traditionally been based on population statistics. However, these predictions have little meaning for the individual patient. We report the training of neural networks to predict outcome for individual patients from one institution and their predictive performance on data from a different institution in another region.
  
  
  METHODS
  5-year follow-up data from 334 patients treated for colorectal cancer were used to train and validate six neural networks designed for the prediction of death within 9, 12, 15, 18, 21, and 24 months. The previously trained 12-month neural network was then applied to 2-year follow-up data from patients from a second institution; outcome was concealed. No further training of the neural network was undertaken. The network's predictions were compared with those of two consultant colorectal surgeons supplied with the same data.
  
  
  RESULTS
  All six neural networks were able to achieve overall accuracy greater than 80% for the prediction of death for individual patients at institution 1 within 9, 12, 15, 18, 21, and 24 months. The mean sensitivity and specificity were 60% and 88%. When the neural network trained to predict death within 12 months was applied to data from the second institution, overall accuracy of 90% (95% CI 84-96) was achieved, compared with the overall accuracy of the colorectal surgeons of 79% (71-87) and 75% (66-84).
  
  
  CONCLUSIONS
  The neural networks were able to predict outcome for individual patients with colorectal cancer much more accurately than the currently available clinicopathological methods. Once trained on data from one institution, the neural networks were able to predict outcome for patients from an unrelated institution.
authors.affiliation: Department of Computer Science, University of Hull.
authors.forename: L
authors.initials: L
authors.lastname: Bottaci
authors.forename: P J
authors.initials: PJ
authors.lastname: Drew
authors.forename: J E
authors.initials: JE
authors.lastname: Hartley
authors.forename: M B
authors.initials: MB
authors.lastname: Hadfield
authors.forename: R
authors.initials: R
authors.lastname: Farouk
authors.forename: P W
authors.initials: PW
authors.lastname: Lee
authors.forename: I M
authors.initials: IM
authors.lastname: Macintyre
authors.forename: G S
authors.initials: GS
authors.lastname: Duthie
authors.forename: J R
authors.initials: JR
authors.lastname: Monson
mesh_terms: D015982:Bias; D015179:Colorectal Neoplasms / Q000401:mortality* / Q000628:therapy*; D005500:Follow-Up Studies; D006801:Humans; D016013:Likelihood Functions; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D016896:Treatment Outcome