Blood Test Developed Using Machine Learning Enables Early-Stage Pancreatic Cancer Detection and Disease Staging
May 04, 2020 | Biotechnology
Reading time: 3-4 minutes
Today, Lab.Equipment has curated a new Biotech article first published on Genetic Engineering and Biotechnology News (GEN):
A multidisciplinary team at the University of Pennsylvania has used machine learning to develop a liquid biopsy that can detect early stages of the most common form of pancreatic cancer by measuring multiple biomarkers in a single blood sample. In a blinded study, the multi-analyte test was found to be more accurate than single biomarker analysis at detecting pancreatic ductal adenocarcinoma (PDAC), and was also better at staging disease than stand-alone imaging.
“Although a larger validation study is needed, this test may improve early disease detection and, when performed in addition to diagnostic imaging, patient selection for curative intent surgery,” the investigators concluded in their published paper in Clinical Cancer Research, which is titled, “A multi-analyte panel consisting of extracellular vesicle miRNAs and mRNA, cfDNA, and CA19-9 shows utility for diagnosis and staging of pancreatic cancer.” The research team included scientists at the Perelman School of Medicine, the Abramson Cancer Center, and the School of Engineering and Applied Science.
PDAC is the third leading cause of cancer deaths in the United States, with an overall five-year survival rate of 9%. Cases are commonly not diagnosed until the disease has already spread, and most patients survive for less than one year following their diagnosis.
If the disease is caught early, patients may be candidates for surgery to remove the cancer, which can be curative. For patients with locally advanced disease that has not spread beyond the pancreas, but who are not candidates for surgery based on the size or location of the tumor, treatment involves three months of systemic therapy like chemo- or radiation therapy, then reassessment to see if surgery is an... Find the complete article at the GEN Blog.
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