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通过拉曼光谱对发育异常组织进行体内检测。

In vivo detection of dysplastic tissue by Raman spectroscopy.

作者信息

Bakker Schut T C, Witjes M J, Sterenborg H J, Speelman O C, Roodenburg J L, Marple E T, Bruining H A, Puppels G J

机构信息

Erasmus Medical Center Rotterdam, The Netherlands.

出版信息

Anal Chem. 2000 Dec 15;72(24):6010-8. doi: 10.1021/ac000780u.

Abstract

The detection of dysplasia and early cancer is important because of the improved survival rates associated with early treatment of cancer. Raman spectroscopy is sensitive to the changes in molecular composition and molecular conformation that occur in tissue during carcinogenesis, and recent developments in fiber-optic probe technology enable its application as an in vivo technique. In this study, the potential of Raman spectroscopy for in vivo classification of normal and dysplastic tissue was investigated. A rat model was used for this purpose, in which dysplasia in the epithelium of the palate was induced by topical application of the carcinogen 4-nitroquinoline 1-oxide. High quality in vivo spectra of normal and dysplastic rat palate tissue, obtained using signal integration times of 100 s were used to create tissue classification models based on multivariate statistical analysis methods. These were tested with an independent set of in vivo spectra, obtained using signal collection times of 10 s. The best performing model, in which signal variance due to signal contributions of the palatal bone was eliminated, was able to distinguish between normal tissue, low-grade dysplasia, and high-grade dysplasia/carcinoma in situ with a selectivity of 0.93 and a sensitivity of 0.78 for detecting low-grade dysplasia and a specificity of 1 and a sensitivity of 1 for detecting high-grade dysplasia/ carcinoma in situ.

摘要

由于早期治疗癌症可提高生存率,因此检测发育异常和早期癌症非常重要。拉曼光谱对致癌过程中组织内发生的分子组成和分子构象变化敏感,并且光纤探头技术的最新进展使其能够作为一种体内技术应用。在本研究中,研究了拉曼光谱对正常组织和发育异常组织进行体内分类的潜力。为此使用了大鼠模型,通过局部应用致癌物4-硝基喹啉1-氧化物诱导腭上皮发育异常。使用100秒的信号积分时间获得的正常和发育异常大鼠腭组织的高质量体内光谱,用于基于多元统计分析方法创建组织分类模型。这些模型用一组独立的体内光谱进行测试,这些光谱使用10秒的信号采集时间获得。表现最佳的模型消除了由于腭骨信号贡献导致的信号方差,能够区分正常组织、低级别发育异常和高级别发育异常/原位癌,检测低级别发育异常的选择性为0.93,灵敏度为0.78,检测高级别发育异常/原位癌的特异性为1,灵敏度为1。

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