Mahadevan-Jansen A, Mitchell M F, Ramanujam N, Malpica A, Thomsen S, Utzinger U, Richards-Kortum R
Biomedical Engineering Program, University of Texas, Austin, USA.
Photochem Photobiol. 1998 Jul;68(1):123-32. doi: 10.1562/0031-8655(1998)068<0123:nirsfv>2.3.co;2.
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 1-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.
在本研究中，我们探究了近红外拉曼光谱区分宫颈癌前病变与正常组织、炎症及化生的潜力，并对低级别和高级别癌前病变进行鉴别诊断。从18例患者的36份活检组织中体外测量了近红外拉曼光谱。开发了检测算法并相对于组织病理学检查进行评估。研究了基于经验选择的峰强度、峰强度比值以及用于数据降维的主成分分析和用于分类的Fisher判别分析相结合的算法。从潜在发色团的测量光谱中初步识别出光谱峰。经验选择的归一化强度可区分癌前病变与其他组织，平均灵敏度和特异性分别为88±4%和92±4%。未归一化强度的比值可区分癌前病变与其他组织，灵敏度和特异性分别为82%和88%，区分高级别与低级别病变的灵敏度和特异性为100%。使用多变量方法，在无偏检验中，八个频率处的强度可用于区分癌前病变与所有其他组织，灵敏度和特异性分别为82%和92%。拉曼算法有可能将炎症和化生等良性异常与癌前病变区分开来。将组织光谱与已发表和测量的发色团光谱进行比较表明，组织光谱最可能的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，近红外拉曼光谱可用于宫颈癌前病变的诊断，并且可能能够准确地将有炎症和化生的样本与癌前病变区分开来。
