Abstract #3482
A single artificial neural network solution to detect pancreatic and lung cancer from high-resolution 1H MR plasma/serum spectra
Meiyappan Solaiyappan1, Santosh Kumar Bharti1, Mohamad Dbouk2, Wasay Nizam3, Malcolm V. Brock3,4, Michael G. Goggins2,4,5, and Zaver M. Bhujwalla1,2,6
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 Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 5Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 6Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
Synopsis
Early detection of cancers using blood-based analytes for routine screening is a rapidly advancing area. Here, we developed a neural-network based solution to detect pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer (NSCLC) with high sensitivity and specificity using human plasma and serum samples to discriminate between subjects with no known pancreatic or lung disease, subjects with benign disease and subjects with PDAC or NSCLC.
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