Application of SELDI-TOF-MS Coupled With an Artificial Neural Network Model to the Diagnosis of Pancreatic Cancer

Background: There are no satisfactory biomarkers for screening pancreatic cancer. The surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique has been used to identify biomarkers for different types of cancers. A new and reliable SELDI proteomic method for diagnosing pancreatic cancer is needed. Methods: Four hundred and fifty-five serum samples were tested by SELDI-TOF-MS matching on a gold chip. Samples were assigned to 1 of 2 subsets according to collection order, viz., a training set, and a testing set. The training set was used to identify statistically significant peaks and to develop the artificial neural network (ANN) model for diagnosing pancreatic cancer. The testing set was used in a blind test to validate the diagnostic efficiency of the ANN model. Results: A total of 62 proteins that differed between patients and controls were identified ( P