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Abstract #4823

Neural-network discrimination of human plasma samples to detect pancreatic cancer

Meiyappan Solaiyappan1, Santosh K Bharti1, Mohamad Dbouk2, Paul T Winnard1, Michael Goggins2,3, and Zaver M. Bhujwalla1,3,4
1The Russell H. Morgan Dept of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2Departments of Pathology and Medicine, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 43Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States

The insidious growth of pancreatic cancer is a major factor contributing to its lethality. Only 10-15% of pancreatic cancers are resectable by the time they are detected. Early detection of pancreatic cancer through routine screening is clearly an unmet clinical need. Here we have applied neural network analysis to 1H magnetic resonance spectra of human plasma samples to differentiate between healthy subjects (control), subjects with benign lesions, and subjects with pancreatic ductal adenocarcinoma (PDAC). Our data support developing a neural-network approach to identify PDAC from 1H MRS of plasma samples.

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