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血液和唾液的融合拉曼光谱分析对头颈癌诊断具有高准确性。

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics.

机构信息

Biomedical Engineering, University of California, Davis, CA, USA.

Electrical and Computer Engineering, University of California, Davis, CA, USA.

出版信息

Sci Rep. 2022 Nov 2;12(1):18464. doi: 10.1038/s41598-022-22197-x.

Abstract

As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

摘要

作为一种快速、无标记、无损的分析测量方法,几乎不需要样品制备,拉曼光谱在液体活检癌症检测和诊断方面显示出巨大的应用前景。我们对来自 50 多名头颈部癌症患者和良性对照组(例如,良性口腔肿块患者)的血浆和唾液进行了拉曼分析和质谱分析。我们构建了无监督数据模型来评估诊断性能。来自任一种生物流体的拉曼光谱均可提供中等性能,用于区分癌症样本。然而,通过融合每位患者的血浆和唾液的拉曼光谱,随后的分析模型提供了令人印象深刻的灵敏度、特异性和准确率,分别为 96.3%、85.7%和 91.7%。我们进一步证实,驱动我们模型中拉曼光谱差异的代谢物与驱动质谱模型的代谢物相同,统一了这两种技术,并验证了拉曼评估代谢物组成的潜在能力。这项研究通过探测来自不同生物流体源的独特化学成分,支持了拉曼在提供附加价值方面的相关性。最终,我们表明,融合血浆和唾液光谱的简单数据扩充常规提供了比单独使用任何一种生物流体更高的临床价值,推动了拉曼光谱用于液体活检癌症诊断的临床转化的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdef/9630497/6aca06ec2206/41598_2022_22197_Fig1_HTML.jpg

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