BACKGROUND: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. METHODS: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study. RESULTS: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers. CONCLUSIONS: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. IMPACT: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.
RCT Entities:
BACKGROUND: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. METHODS: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study. RESULTS: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers. CONCLUSIONS: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. IMPACT: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.
Authors: Fan Zhang; Yuanyuan Zhang; Chaofu Ke; Ang Li; Wenjie Wang; Kai Yang; Huijuan Liu; Hongyu Xie; Kui Deng; Weiwei Zhao; Chunyan Yang; Ge Lou; Yan Hou; Kang Li Journal: Metabolomics Date: 2018-04-26 Impact factor: 4.290
Authors: A W L Bayci; D A Baker; A E Somerset; O Turkoglu; Z Hothem; R E Callahan; R Mandal; B Han; T Bjorndahl; D Wishart; R Bahado-Singh; S F Graham; R Keidan Journal: Metabolomics Date: 2018-08-03 Impact factor: 4.290