高等学校化学学报 ›› 2013, Vol. 34 ›› Issue (12): 2721.doi: 10.7503/cjcu20130131

• 分析化学 • 上一篇    下一篇

新鲜乳腺癌组织的近红外拉曼光谱分析

胡成旭1, 郑超2, 张海鹏2, 毕丽荣2, 徐抒平1, 范志民2, 韩冰2, 徐蔚青1   

  1. 1. 吉林大学超分子结构与材料国家重点实验室, 长春 130012;
    2. 吉林大学第一医院, 长春 130021
  • 收稿日期:2013-02-04 出版日期:2013-12-10 发布日期:2013-11-01
  • 作者简介:徐蔚青,男,博士,教授,博士生导师,主要从事光纤光谱传感器研究与应用. E-mail:xuwq@jlu.edu.cn;韩 冰,男,博士,主治医师,主要从事乳腺癌光谱学研究及乳腺癌内分泌治疗的基础研究. E-mail:yintian77@126.com
  • 基金资助:

    国家自然科学基金(批准号:81202078,20903043,20973075,21073073,91027010)资助.

Study on Fresh Breast Tissues by Near-infrared Raman Spectroscopy

HU Cheng-Xu1, ZHENG Chao2, ZHANG Hai-Peng2, BI Li-Rong2, XU Shu-Ping1, FAN Zhi-Min2, HAN Bing2, XU Wei-Qing1   

  1. 1. State Key Laboratory for Supramolecular Structure and Materials, Jilin University, Changchun 130012, China;
    2. The First Hospital of Jilin University, Changchun 130021, China
  • Received:2013-02-04 Online:2013-12-10 Published:2013-11-01

摘要:

采用便携式拉曼光谱仪对正常、良性和恶性的乳腺癌组织进行检测,通过对其拉曼光谱的指认,归纳了其主要区别和特征. 在3类乳腺组织中有明显的脂类的特征峰(1230,1268,1301,1440和1743 cm-1),而在良性和恶性的组织中,则出现了较为明显的蛋白(1246,1271,1315和1364 cm-1)和核酸(1340 cm-1)的特征峰. 良性和恶性组织的区别在于恶性组织特有的特征峰(1340 cm-1),而良性组织所特有的特征峰则应归属为蛋白. 在数据分析过程中,选择能够反映样本化学本质的特征峰,利用高斯过程的机器学习对特征峰值建立模型. 特异性(0.94)、灵敏度(0.95)和Matthews相关系数(0.86)表明在模型中3种组织有比较良好的辨别度,对于应用拉曼光谱方法辨别正常和患病乳腺组织具有参考价值.

关键词: 拉曼光谱, 乳腺癌, 高斯过程

Abstract:

A portable Raman spectrometer was used for distinguishing the characteristics of normal, malignant and benign fresh breast biopsy samples. Based on spectral profiles, the presence of lipids(1230, 1268, 1301, 1440, 1743 cm-1) is indicated in normal tissue. And proteins(amide Ⅰ, and amide Ⅲ, 1246, 1271, 1315, 1364 cm-1) are found in benign and malignant tissues. Between benign and malignant, nucleic acids(1340 cm-1) are found to be good discrimination parameters. In the process of data analysis, the model was set up by Gaussian Process with the intensity of the feature, and obtained the specificity(0.94), sensibility(0.95) and Matthews correlation coefficient(MCC, 0.86). This study shows the significance in diagnosing breast disease, and contributes fundamentally to further application on clinic.

Key words: Raman spectroscopy, Breast cancer, Guassian process

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