title: Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification.
abstract: OBJECTIVE
  In order to improve the prognosis of pancreatic cancer patients, it is crucial to explore novel tools for its early diagnosis. Here, we attempted to screen serum biomarkers to distinguish pancreatic cancer from non-cancer individuals.
  
  
  METHODS
  47 serum samples from pancreatic cancer patients, 39 of whom had small surgically resectable cancers, were collected before surgery, and an additional 53 serum samples from age- and sex-matched individuals without cancer were used as controls. The surface-enhanced laser desorption/ionization (SELDI) ProteinChip was applied to analyze serum protein profiling. 54 samples (27 with pancreatic cancer and 27 controls) were analyzed in the training set by a decision tree algorithm to be able to separate pancreatic cancer from controls. A double-blind test was used to determine the sensitivity and specificity of the classification model.
  
  
  RESULTS
  A panel of six biomarkers was selected to set up a decision tree as the classification model. The model separated effectively pancreatic cancer from control samples, achieving a sensitivity of 88.9% and a specificity of 74.1%. The double-blind test challenged the model with a sensitivity of 80% and a specificity of 84.6%.
  
  
  CONCLUSIONS
  The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.
authors.affiliation: Department of Pathophysiology, Shanghai Terry Fox Cancer Center and Institute of Hematology, Rui-Jin Hospital, Shanghai Second Medical University, Shanghai 200025, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Yu
authors.forename: Sheng
authors.initials: S
authors.lastname: Chen
authors.forename: Li-Shun
authors.initials: LS
authors.lastname: Wang
authors.forename: Wen-Li
authors.initials: WL
authors.lastname: Chen
authors.forename: Wei-Jian
authors.initials: WJ
authors.lastname: Guo
authors.forename: Hua
authors.initials: H
authors.lastname: Yan
authors.forename: Wei-Hua
authors.initials: WH
authors.lastname: Zhang
authors.forename: Cheng-Hong
authors.initials: CH
authors.lastname: Peng
authors.forename: Sheng-Dao
authors.initials: SD
authors.lastname: Zhang
authors.forename: Hong-Wei
authors.initials: HW
authors.lastname: Li
authors.forename: Guo-Qiang
authors.initials: GQ
authors.lastname: Chen
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010190:Pancreatic Neoplasms / Q000097:blood*; D011237:Predictive Value of Tests; D040081:Protein Array Analysis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D014408:Biomarkers, Tumor