Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

ABSTRACT: The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% acc

SUBMITTER: Unger K 

PROVIDER: S-EPMC5955422 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature