a review of surface-enhanced raman spectroscopy on potential clinical applications towards diagnosing colorectal cancer

Colorectal cancer (crc) is one of the leading cancers in the world and early-screening is still the best method of cancer patient survival. however, colonoscopy as the current gold standard is not without flaws and an emerging technique called surface-enhanced raman spectroscopy (sers) coupled with machine learning is a possible candidate that could be applied in parallel with colonoscopy. this paper looks into the principles of sers along with one of the most used machine learning algorithms: principal component analysis (pca), and linear discriminate analysis (lda). case studies will be presented in the sers application towards early screening, targeted imaging, and alternative diagnosis. the paper will conclude with the authors’ analysis of the current landscape of sers implementation into clinical applications. this review article highlights the promising technology of sers as a potentially useful tool for clinicians and calls their attention toward this emerging technology.