Machine Intelligence-Driven Classification of Cancer Patients-Derived Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results from a Pilot Study
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Patient-derived extracellular vesicles (EVs) that contains a complex biological cargo is a valuable source of liquid biopsy diagnostics to aid in early detection, cancer screening, and precision nanotherapeutics. In this study, we predicted that coupling cancer patient blood-derived EVs to time-resolved spectroscopy and artificial intelligence (AI) could provide a robust cancer screening and follow-up tools. Methods: Fluorescence correlation spectroscopy (FCS) measurements were performed on 24 blood samples-derived EVs. Blood samples were obtained from 15 cancer patients (presenting 5 different types of cancers), and 9 healthy controls (including patients with benign lesions). The obtained FCS autocorrelation spectra were processed into power spectra using the Fast-Fourier Transform algorithm and subjected to various machine learning algorithms to distinguish cancer spectra from healthy control spectra. Results and Applications: The performance of AdaBoost Random Forest (RF) classifier, support vector machine, and multilayer perceptron, were tested on selected frequencies in the N=118 power spectra. The RF classifier exhibited a 90 distinguishing the FCS power spectra of cancer patients from those of healthy controls. Further, an image convolutional neural network (CNN), ResNet network, and a quantum CNN were assessed on the power spectral images as additional validation tools. All image-based CNNs exhibited a nearly equal classification performance with an accuracy of roughly 82 specificity scores. Our pilot study demonstrates that AI-algorithms coupled to time-resolved FCS power spectra can accurately and differentially classify the complex patient-derived EVs from different cancer samples of distinct tissue subtypes.
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