Prediction of survival from XXXX and oesophago-gastric junction following surgical resection using an artificial neural network.

@entity1 @entity5 @entity189 @entity6291 @entity356
AIM: The aim of this study was to assess the ability of artificial neural network (ANN) in predicting survival in @entity1 undergoing surgical resection for @entity189 and oesophago-gastric junction. METHODS: From January 1995 to August 2004 @entity1 who underwent surgery for oesophageal and @entity356 were identified. Biographical data, body mass index and pathological minimal @entity5 dataset were used to design an ANN. Post-operative survival was assessed at 1 and 3 years. Sixty percent of data was used to train and validate the ANN and 40% was used to evaluate the accuracy of trained ANN in predicting survival. This was compared with Union Internacional Contra la Cancrum UICC TNM classification system. RESULTS: Two hundred and sixteen @entity1 underwent resectional surgery for oesophageal and OGJ @entity189 . The accuracy of the ANN in predicting survival at 1 and 3 years was 88% (sensitivity: 92.3%, specificity: 84.5%, DP = 2.3) and 91.5% (sensitivity of 94.61%, specificity: 88%, DP = 2.72), respectively. These figures were significantly better than 1- and 3-year survival predictions using the UICC TNM classification system 71.6% (sensitivity of 66.4%, specificity: 75.5%, and @entity6291 ) and 74.7% (sensitivity of 70.5%, specificity: 74.9%, @entity6291 ), respectively (P < 0.01) (P < 0.05). CONCLUSION: ANNs are superior to the UICC TNM classification system in correlating with survival following resection of @entity189 and OG junction and can become valuable tools in the management of @entity1 with @entity189 .
