Prediction of survival from carcinoma of oesophagus and oesophago-gastric junction following surgical resection using an artificial neural network.
OBJECTIVE
The aim of this study was to assess the ability of artificial neural network (ANN) in predicting survival in patients undergoing surgical resection for carcinoma of oesophagus and oesophago-gastric junction.


METHODS
From January 1995 to August 2004 patients who underwent surgery for oesophageal and gastric carcinoma were identified. Biographical data, body mass index and pathological minimal cancer 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 patients underwent resectional surgery for oesophageal and OGJ carcinoma. 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 DP < 1) and 74.7% (sensitivity of 70.5%, specificity: 74.9%, DP < 1), respectively (P < 0.01) (P < 0.05).


CONCLUSIONS
ANNs are superior to the UICC TNM classification system in correlating with survival following resection of carcinoma of oesophagus and OG junction and can become valuable tools in the management of patients with oesophageal carcinoma.
Mofidi
R
R
Department of Surgery, Royal Infirmary of Edinburgh, UK. rmofidi@doctors.net.uk
Deans
C
C
Duff
M D
MD
de Beaux
A C
AC
Paterson Brown
S
S
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D015992:Body Mass Index; D002277:Carcinoma / Q000473:pathology / Q000601:surgery*; D004938:Esophageal Neoplasms / Q000473:pathology / Q000601:surgery*; D004943:Esophagogastric Junction / Q000473:pathology / Q000601:surgery*; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting; D006801:Humans; D008207:Lymphatic Metastasis / Q000473:pathology; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging / Q000379:methods; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013274:Stomach Neoplasms / Q000473:pathology / Q000601:surgery*; D015996:Survival Rate; D016896:Treatment Outcome