title: Artificial neural networks--a method for prediction of survival following liver resection for colorectal cancer metastases.
abstract: OBJECTIVE
  To construct an artificial neural network (ANN) model to predict survival after liver resection for colorectal cancer (CRC) metastases.
  
  
  BACKGROUND
  CRC liver metastases are fatal if untreated and resection can possibly be curative. Predictive models stratify patients into risk categories to predict prognosis and select those who can benefit from aggressive multidisciplinary treatment and intensive follow-up. Standard linear models assume proportional hazards, whereas more flexible non-linear survival models based on ANNs may better predict individual long-term survival.
  
  
  METHODS
  Clinicopathological and perioperative data on patients who underwent liver resection for CRC metastases between 1994 and 2009 were studied retrospectively. A five-fold cross-validated ANN model was constructed. Risk variables were ranked and minimised through calibrated ANNs. Time dependent hazard ratio (HR) was calculated using the ANN. Performance of the ANN model and Cox regression were analysed using Harrell's C-index.
  
  
  RESULTS
  241 patients with a median age of 66 years were included. There were no perioperative deaths and median survival was 56 months. Of 28 potential risk variables, the ANN selected six: age, preoperative chemotherapy, size of largest metastasis, haemorrhagic complications, preoperative CEA-level and number of metastases. The C-index was 0.72 for the ANN model and 0.66 for Cox regression.
  
  
  CONCLUSIONS
  For the first time ANNs were used to successfully predict individual long-term survival for patients following liver resection for CRC metastases. In the future, more complex prognostic factors can be incorporated into the ANN model to increase its predictive ability.
authors.affiliation: Department of Surgery, Clinical Sciences Lund, Lund University and Skåne University Hospital, Lund, Sweden.
authors.forename: L
authors.initials: L
authors.lastname: Spelt
authors.forename: J
authors.initials: J
authors.lastname: Nilsson
authors.forename: R
authors.initials: R
authors.lastname: Andersson
authors.forename: B
authors.initials: B
authors.lastname: Andersson
mesh_terms: D000328:Adult; D000368:Aged; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D015179:Colorectal Neoplasms / Q000401:mortality* / Q000473:pathology*; D005260:Female; D006498:Hepatectomy / Q000401:mortality; D006801:Humans; D053208:Kaplan-Meier Estimate; D008113:Liver Neoplasms / Q000188:drug therapy / Q000401:mortality* / Q000556:secondary / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy / Q000379:methods; D016571:Neural Networks, Computer; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012307:Risk Factors