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Artificial neural networks predict survival from pancreatic cancer after radical surgery
Daniel Ansari1, Johan Nilsson, Roland Andersson
1Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
Background:
Artificial neural networks (ANNs) are nonlinear pattern recognition techniques that can be used as a tool in medical decision making. The objective of this study was to develop an ANN model for predicting survival in patients with pancreatic ductal adenocarcinoma (PDAC).
Methods:
A flexible nonlinear survival model based on ANNs was designed by using clinical and histopathological data from 84 patients who underwent resection for PDAC.
Results:
Seven of 33 potential risk variables were selected to construct the ANN, including lymph node metastasis, differentiation, body mass index, age, resection margin status, peritumoral inflammation, and American Society of Anesthesiologists grade. Three variables (ie, lymph node metastasis, leukocyte count, and tumor location) were significant according to Cox regression analysis. Harrell's concordance index for the ANN model was .79, and for Cox regression it was .67.
Conclusions:
For the first time, ANNs have been used to successfully predict individual long-term survival for patients after radical surgery for PDAC.
