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%. CONCLUSION: The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.
OBJECTIVE: In order to improve the prognosis of pancreatic cancerpatients, 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 cancerpatients, 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%. CONCLUSION: The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.
Authors: James R Hocker; Russell G Postier; Min Li; Megan R Lerner; Stan A Lightfoot; Marvin D Peyton; Subrato J Deb; Candace M Baker; Travis L Williams; Rushie Jane Hanas; Donald E Stowell; Theresa J Lander; Daniel J Brackett; Jay S Hanas Journal: Cancer Lett Date: 2015-01-28 Impact factor: 8.679
Authors: A Xue; C J Scarlett; L Chung; G Butturini; A Scarpa; R Gandy; S R Wilson; R C Baxter; R C Smith Journal: Br J Cancer Date: 2010-06-29 Impact factor: 7.640
Authors: James R Hocker; Altaf Mohammed; Christopher E Aston; Misty Brewer; Stan A Lightfoot; Chinthalapally V Rao; Jay S Hanas Journal: Int J Cancer Date: 2013-07-01 Impact factor: 7.396
Authors: Kyoko Kojima; Senait Asmellash; Christopher A Klug; William E Grizzle; James A Mobley; John D Christein Journal: J Gastrointest Surg Date: 2008-08-15 Impact factor: 3.452