机器学习驱动的基于荧光相关光谱的癌症患者来源细胞外囊泡分类：一项初步研究结果
Machine Intelligence-Driven Classification of Cancer Patients-Derived Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results from a Pilot Study
AI总结：
本研究将患者血液来源细胞外囊泡的荧光相关光谱功率谱与机器学习结合，实现癌症与健康对照的高准确率分类，RF分类器准确率达90%，CNN类模型约82%，为癌症筛查提供新工具。
摘要：
患者来源的细胞外囊泡（EVs）含有复杂的生物货物，是液体活检诊断的宝贵来源，有助于早期检测、癌症筛查和精准纳米治疗。在本研究中，我们预测将癌症患者血液来源的EVs与时间分辨光谱和人工智能（AI）相结合，可以提供稳健的癌症筛查和随访工具。方法：对24份血液样本来源的EVs进行了荧光相关光谱（FCS）测量。血液样本来自15名癌症患者（呈现5种不同类型的癌症）和9名健康对照者（包括良性病变患者）。使用快速傅里叶变换算法将获得的FCS自相关光谱处理成功率谱，并应用各种机器学习算法来区分癌症光谱与健康对照光谱。结果与应用：在N=118个功率谱的选定频率上测试了AdaBoost随机森林（RF）分类器、支持向量机和多层感知器的性能。RF分类器在区分癌症患者与健康对照者的FCS功率谱方面表现出90%的分类准确率以及高灵敏度和特异性。此外，作为额外的验证工具，在功率谱图像上评估了图像卷积神经网络（CNN）、ResNet网络和量子CNN。所有基于图像的CNN均表现出几乎相同的分类性能，准确率约为82%，灵敏度和特异性得分也相当高。我们的初步研究表明，AI算法与时间分辨FCS功率谱相结合，可以准确且区分性地分类来自不同组织亚型的不同癌症样本的复杂患者来源EVs。
英文摘要：
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. 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% classification accuracy and high sensitivity and specificity in 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% 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.
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