Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips.

Specificity is a challenge in liquid biopsy and early diagnosis of various diseases. There are only a few biomarkers that have been approved for use in cancer diagnostics; however, these biomarkers suffer from a lack of high specificity. Moreover, determining the exact type of disorder for patients with positive liquid biopsy tests is difficult, especially when the aberrant expression of one single biomarker can be found in various other disorders.

Multiplex detection of pancreatic cancer biomarkers using a SERS-based immunoassay.

Early diagnosis of pancreatic cancer (PC) is critical to reduce the mortality rate of this disease. Current biological analysis approaches cannot robustly detect several low abundance PC biomarkers in sera, limiting the clinical application of these biomarkers. Enzyme linked immunosorbent assay and radioimmunoassay are two common platforms for detection of biomarkers; however, they suffer from some limitation.