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The early detection of cancer is a challenging problem in medicine. The bloodsera of cancer patients are enriched with heterogeneous secretory lipid boundextracellular vesicles (EVs), which present a complex repertoire of informationand biomarkers, representing their cell of origin, that are being currentlystudied in the field of liquid biopsy and cancer screening. Vibrationalspectroscopies provide non-invasive approaches for the assessment of structuraland biophysical properties in complex biological samples. In this pilot study,multiple Raman spectroscopy measurements were performed on the EVs extractedfrom the blood sera of 9 patients consisting of four different cancer subtypes(colorectal cancer, hepatocellular carcinoma, breast cancer and pancreaticcancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared)spectroscopy measurements were performed as a complementary approach to Ramananalysis, on two of the four cancer subtypes. The AdaBoost Random ForestClassifier, Decision Trees, and Support Vector Machines (SVM) distinguished thebaseline corrected Raman spectra of cancer EVs from those of healthy controls(18 spectra) with a classification accuracy of above 90 percent when reduced toa spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50training: testing split. FTIR classification accuracy on 14 spectra showed an80 percent classification accuracy. Our findings demonstrate that basic machinelearning algorithms are powerful applied intelligence tools to distinguish thecomplex vibrational spectra of cancer patient EVs from those of healthypatients. These experimental methods hold promise as valid and efficient liquidbiopsy for artificial intelligence-assisted early cancer screening.

Machine Learning Characterization of Cancer Patients-Derived Extracellular Vesicles using Vibrational Spectroscopies