Diagnosis of pancreatic carcinoma based on combined measurement of multiple serum tumor markers using artificial neural network analysis

Background Artificial neural network (ANN) has demonstrated the ability to assimilate information from multiple sources to enable the detection of subtle and complex patterns. In this research, we evaluated an ANN model in the diagnosis of pancreatic cancer using multiple serum markers. Methods In this retrospective analysis, 913 serum specimens collected at the Department of General Surgery of Beijing Friendship Hospital were analyzed for carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA). The three tumor marker values were used as inputs into an ANN and randomized into a training set of 658 (70.31% were malignant) and a test set of the remaining 255 samples (70.69% were malignant). The samples were also evaluated using a Logistic regression (LR) model. Results The ANN-derived composite index was superior to each of the serum tumor markers alone and the Logistic regression model. The areas under receiver operating characteristic curves (AUROC) was 0.905 (95% confidence Interval (Cl) 0.868-0.942) for ANN, 0.812 (95% Cl 0.762-0.863) for the Logistic regression model, 0.845 (95% Cl 0.798-0.893) for CA19-9, 0.795 (95% Cl 0.738-0.851) for CA125, and 0.800 (95% Cl 0.746-0.854) for CEA. ANN analysis of multiple markers yielded a high level of diagnostic accuracy (83.53%) compared to LR (74.90%). Conclusion The performance of ANN model in the diagnosis of pancreatic cancer is better than the single tumor marker and LR model.