  The early detection of cancer is a challenging problem in medicine. The blood
sera of cancer patients are enriched with heterogeneous secretory lipid bound
extracellular vesicles (EVs), which present a complex repertoire of information
and biomarkers, representing their cell of origin, that are being currently
studied in the field of liquid biopsy and cancer screening. Vibrational
spectroscopies provide non-invasive approaches for the assessment of structural
and biophysical properties in complex biological samples. In this pilot study,
multiple Raman spectroscopy measurements were performed on the EVs extracted
from the blood sera of 9 patients consisting of four different cancer subtypes
(colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic
cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared)
spectroscopy measurements were performed as a complementary approach to Raman
analysis, on two of the four cancer subtypes. The AdaBoost Random Forest
Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the
baseline corrected Raman spectra of cancer EVs from those of healthy controls
(18 spectra) with a classification accuracy of above 90 percent when reduced to
a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50
training: testing split. FTIR classification accuracy on 14 spectra showed an
80 percent classification accuracy. Our findings demonstrate that basic machine
learning algorithms are powerful applied intelligence tools to distinguish the
complex vibrational spectra of cancer patient EVs from those of healthy
patients. These experimental methods hold promise as valid and efficient liquid
biopsy for artificial intelligence-assisted early cancer screening.
