Machine intelligence-driven classification of cancer patients-derived extracellular vesicles using fluorescence correlation spectroscopy: results from a pilot study

被引:0
作者
Abicumaran Uthamacumaran
Mohamed Abdouh
Kinshuk Sengupta
Zu-hua Gao
Stefano Forte
Thupten Tsering
Julia V. Burnier
Goffredo Arena
机构
[1] Concordia University,Department of Physics (Alumni)
[2] McGill Genome Center (Majewski Lab),The Henry C. Witelson Ocular Pathology Laboratory
[3] Cancer Research Program,Department of Pathology and Laboratory Medicine
[4] Research Institute of the McGill University Health Centre,Gerald Bronfman Department of Oncology
[5] McGill University,Experimental Pathology Unit, Department of Pathology
[6] Microsoft Research and Development,Department of Surgery
[7] University of British Columbia,undefined
[8] IOM Ricera,undefined
[9] McGill University,undefined
[10] McGill University,undefined
[11] Istituto Mediterraneo Di Oncologia,undefined
[12] McGill University,undefined
[13] Fondazione Gemelli-Giglio,undefined
[14] Contrada Pollastra,undefined
来源
Neural Computing and Applications | 2023年 / 35卷
关键词
Cancer; Patient serum; Extracellular vesicles (EVs); Liquid biopsy; Diagnostics; Spectroscopy; Machine learning; Artificial intelligence; Precision nanomedicine; Systems oncology;
D O I
暂无
中图分类号
学科分类号
摘要
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. In our pilot study, fluorescence correlation spectroscopy (FCS) measurements were taken on 24 blood samples-derived EVs. Blood samples were obtained from 15 cancer patients (presenting five different types of cancers), and nine healthy controls (including patients with benign lesions). EVs samples were labeled with PKH67 dye. The obtained FCS autocorrelation spectra were processed into power spectra using the fast Fourier transform algorithm. The processed power spectra were subjected to various machine learning algorithms to distinguish cancer spectra from healthy control spectra. 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 the highest classification accuracy and performance metrics in distinguishing the FCS power spectra of cancer patients from those of healthy controls. Further, neural computing via 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 and reasonably high sensitivity and 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. As such, our findings hold promise in the diagnostic and prognostic screening in clinical medicine.
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页码:8407 / 8422
页数:15
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