Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China.
Department of Pathology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China.
Int J Nanomedicine. 2020 Apr 1;15:2303-2314. doi: 10.2147/IJN.S233663. eCollection 2020.
The objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma.
We proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer.
SERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%.
Our results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.
本研究旨在评估表面增强拉曼光谱结合滤膜和先进的多元数据分析在识别和区分良恶性甲状腺肿瘤方面的性能和可行性。
我们提出了一种膜滤表面增强拉曼光谱技术，用于区分良性甲状腺肿瘤和甲状腺癌。也就是说，通过使用最佳孔径的滤膜，对甲状腺肿瘤患者的血浆样本进行预处理，过滤出大分子蛋白，然后进行 SERS 测量。对 102 例甲状腺肿瘤患者（70 例甲状腺癌和 32 例良性甲状腺肿瘤）的血浆超滤液进行 SERS 光谱分析和比较。对背景扣除和归一化以及一阶导数处理后的 SERS 光谱数据进行主成分分析-线性判别分析（PCA-LDA）和套索偏最小二乘判别分析（Lasso-PLS-DA）两种多元统计分析，以分析和比较良性甲状腺肿瘤和甲状腺癌的鉴别诊断。
对总共 102 例甲状腺肿瘤患者（良性甲状腺肿瘤 N=32；甲状腺癌 N=70）的血浆进行了 SERS 测量。通过使用滤膜，有效地过滤出血浆中的大分子蛋白，得到高质量的 SERS 光谱。PCA-LDA 方法的良恶性甲状腺肿瘤鉴别准确率为 84.3%，而 Lasso-PLS-DA 的鉴别准确率为 90.2%。
我们的结果表明，表面增强拉曼光谱结合超滤和多元分析具有提供非侵入性、快速和客观检测和区分良恶性甲状腺肿瘤的潜力。
