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Near-infrared Raman spectroscopy for in vitro detection of cervical precancers

机译:近红外拉曼光谱用于宫颈癌前体的体外检测

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In this study, we investigate the potential of near-infrared Raman spectroscopy to differentiate cervical precancers from normal tissues, inflammation and metaplasia and to differentially diagnose low-grade and high-grade precancers. Near infrared Raman spectra were measured from 36 biopsies from 18 patients in vitro, Detection algorithms were developed and evaluated relative to histopathologic examination. Algorithms based on empirically selected peak intensities, ratios of peak intensities and a combination of principal component analysis for data reduction and Fisher discriminant analysis for classification were investigated. Spectral peaks were tentatively identified from measured spectra of potential chromophores. Empirically selected normalized intensities can differentiate precancers from other tissues with an average sensitivity and specificity of 88 +/- 4% and 92 +/- 4%, Ratios of unnormalized intensities can differentiate precancers from other tissues with a sensitivity and specificity of 82% and 88% and high-grade from low-grade lesions with a sensitivity and specificity of 100%. Using multivariate methods, intensities at eight frequencies can be used to differentiate precancers from all other tissues with a sensitivity and specificity of 82% and 92% in an unbiased test. Raman algorithms can potentially separate benign abnormalities such as inflammation and metaplasia from precancers. Comparison of tissue spectra to published and measured chromophore spectra indicate that the most likely primary contributors to the tissue spectra are collagen, nucleic acids, phospholipids and glucose l-phosphate. These results suggest that near-infrared Raman spectroscopy can be used for cervical precancer diagnosis and may be able to accurately separate samples with inflammation and metaplasia from precancer. [References: 39]
机译:在这项研究中,我们调查了近红外拉曼光谱技术有可能将宫颈癌与正常组织,炎症和化生区分开来,并可以鉴别低度和高度癌前体。从18例患者的36例活检样本中测量了近红外拉曼光谱,并开发了检测算法并相对于组织病理学检查进行了评估。研究了基于经验选择的峰强度,峰强度的比率以及将主成分分析用于数据约简和Fisher判别分析用于分类的算法。从潜在发色团的实测光谱中初步鉴定出光谱峰。根据经验选择的归一化强度可以使前癌与其他组织区分开,平均敏感性和特异性分别为88 +/- 4%和92 +/- 4%。未归一化强度的比率可以使前癌与其他组织区分开来,其灵敏度和特异性为82%和88%的病灶来自低等病变,敏感性和特异性为100%。使用多变量方法,可以使用八种频率的强度在无偏测试中以82%和92%的灵敏度和特异性将前癌与所有其他组织区分开。拉曼算法可以将良性异常(例如炎症和化生)与癌前病变区分开。组织光谱与已公开和测得的生色团光谱的比较表明,最可能的组织光谱主要贡献者是胶原蛋白,核酸,磷脂和磷酸葡萄糖。这些结果表明,近红外拉曼光谱可以用于宫颈癌前诊断,并且可以准确地将具有炎症和化生的样品与癌前分离。 [参考:39]

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