Zheng Xiangxiang, Wu Guohua, Wang Jing, Yin Longfei, Lv Xiaoyi
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
Biomed Opt Express. 2022 Mar 4;13(4):1912-1923. doi: 10.1364/BOE.448121. eCollection 2022 Apr 1.
In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.
在本研究中，我们探讨了使用表面增强拉曼光谱（SERS）结合支持向量机（SVM）算法从健康志愿者中快速鉴别子宫肌瘤和宫颈癌的可行性。记录了30例子宫肌瘤患者、36例宫颈癌患者以及30名健康受试者的血清样本的SERS光谱。使用SVM建立分类模型，并使用三种类型的核函数，即线性、多项式和高斯径向基函数（RBF）进行比较。当采用多项式核函数时，对三组进行分类的总体诊断准确率可达86.5%。此外，当选择最优核函数时，鉴别健康人与子宫肌瘤患者、健康人与宫颈癌患者以及子宫肌瘤患者与宫颈癌患者的诊断准确率分别达到98.3%、93.9%和90.9%。目前的结果表明，血清SERS技术与SVM算法有望成为快速筛查子宫肌瘤和宫颈癌的临床工具。
