期刊文献+

近红外激光拉曼技术在体探测胃癌腹膜播散 被引量:5

Near-Infrared Raman Spectroscopy for Detection of Gastric Cancer Peritoneal Dissemination in vivo
原文传递
导出
摘要 以腹膜接种人胃癌细胞SGC-7901的裸鼠为胃癌腹膜播散的动物模型,进行模拟外科手术,在体探测不同种植期的裸鼠腹膜癌结节及正常腹膜组织的激光拉曼光谱,对比光谱差异,采用支持向量机(SVM)算法对光谱进行分类和分期判决。结果表明,癌结节和正常组织拉曼光谱差异显著,用支持向量机算法进行分类的灵敏度、特异度和诊断准确度分别为95.73%、70.73%和90.73%;不同生长期的癌结节组织拉曼光谱也存在明显差异,用支持向量机算法进行分期的结果分别为98.82%、98.73%和98.78%。从分类结果可以看出,此方法对指导外科手术中癌变组织的识别有重要的意义。 The nude mice injected with human gastric cancer animal models of gastric cancer peritoneal dissemination in this cells (SGC-7901) in their peritoneums are chosen as research. The Raman spectra at 785 nm excitation of both these nude mice which are in different tumor planting periods and the normal counterpart are taken in vivo in the imitate laparotomy. 205 spectra are collected. The spectra of different tissue types are compared and classified by support vector machine (SVM) algorithm. The results show significant differences between normal and malignant tissues. For normal and malignant tissues, the sensitivity, specificity and accuracy are 95. 73%, 70. 73% and 90.73%, respectively, while for different tumor planting periods, they are 98.82%, 98.73% and 98.78%. The experimental results show that Raman spectra differ significantly between cancerous and normal gastric tissues, which provides experimental basis for the diagnosis of gastric cancer by Raman spectroscopy technology. And SVM algorithm can give well generalized classification performance for the samples, which expands the application of mathematical algorithms in classification.
出处 《中国激光》 EI CAS CSCD 北大核心 2011年第9期209-214,共6页 Chinese Journal of Lasers
基金 青岛市科技发展计划(08-1-3-31-jch)资助课题
关键词 光谱学 胃癌 激光拉曼光谱 裸鼠 支持向量机 在体 spectroscopy gastric cancers laser Raman spectroscopy nude mice support vector machines in vivo
  • 相关文献

参考文献13

  • 1S. K. Teh, W. Zheng, K. Y. Ho et al.. Near-infrared Raman spectroscopy for optical diagnosis in the stomach: identification of Helicobacter-pylori infection and intestinal metaplasia[J]. Int. J. Cancer, 2010, 126(8) : 1920-1927.
  • 2高泽红,于晶功,刘福祥,胡波,Michael Guiver.结直肠癌组织中脂类伸缩振动的拉曼光谱[J].中国激光,2010,37(2):605-608. 被引量:8
  • 3Z. Huang, M. S. Bergholt, W. Zheng et al.. In vivo early diagnosis of gastric dysplasia using narrow-band image-guided Raman endoscopy[J]. J. Biomed. Opt. , 2010, 13(5) : 037017.
  • 4S. H. Abigail, V. Zoya, A. G. Joseph et al.. In vivo margin assessment during partial mastectomy breast surgery using Raman spectroscopy[J]. Cancer Res., 2006, 66(6): 3317-3322.
  • 5张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42. 被引量:2289
  • 6S. K. Majumder, N. Ghosh, P. K. Gupta. Support vector machine for optical diagnosis of cancer[J]. J. Biomed. Opt., 2005, 10(2): 024084.
  • 7刘露,刘宛予,楚春雨,吴军,周洋,张红霞,鲍劼.CT图像中肿大淋巴结肺癌转移分类方法[J].电子与信息学报,2009,31(10):2476-2482. 被引量:6
  • 8马君,张奚宁,徐明,毛伟征,郑荣儿.激光诱导荧光技术在体探测裸鼠腹膜胃癌播散[J].中国激光,2009,36(10):2566-2570. 被引量:6
  • 9C. Cortes, V. Vapnik. Support vector networks[J]. Machine Learning, 1995, 20:273-297.
  • 10J. A. Swets. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857): 1285-1293.

二级参考文献59

共引文献2325

同被引文献54

引证文献5

二级引证文献14

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部