Abstract #0374

High-precision neural-network discrimination of human plasma samples to detect pancreatic cancer using specialized data-augmentation method

Meiyappan Solaiyappan¹, Santosh Kumar Bharti¹, Paul T Winnard Jr¹, Mohamad Dbouk², Michael G Goggins^2,3,4, and Zaver M Bhujwalla^1,3,5

¹Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁴Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Department of Radiation 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 ~20% 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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