A Novel Blood-Based Screening Test for Colorectal Cancer Using Raman Spectroscopy.

BACKGROUND: Label-free vibrational spectroscopic techniques (Raman spectroscopy) combined with machine learning (ML) methodologies have huge potential for the development of screening and diagnostic tests in oncology. Traditionally, screening for colorectal cancer (CRC) has relied on immunochemical faecal occult blood (iFOBT) testing; however, this has shown to have low compliance in the Australian setting. The aim of this study was to assess the efficacy of Raman spectroscopy for the detection of CRC and potential screening for CRC. METHODS: Plasma samples were analysed from a total of 370 participants; 117 CRC patients and 253 iFOBT positive control participants. 8 of the CRC patients were from the iFOBT cohort. The control cohort had either advanced adenomas (n = 81), non-advanced adenomas (n = 76), colitis (n = 2) or no evidence of disease (n = 94). The Raman spectra of these plasma samples were analysed using ML algorithms in separate training (n = 222) and validation (n = 148) cohorts. RESULTS: A sensitivity of 84% and specificity of 93% was achieved for the detection of CRC in the validation cohort. When analysed in subcategories, 95% of people with CRC would receive a colonoscopy and 42% of FIT positive people could potentially be spared a colonoscopy. CONCLUSION: This exploratory study demonstrated a high level of accuracy for the detection of CRC using a Raman spectroscopy-based ML model and has the potential to be used for CRC screening.