Feng Xu, Moy Austin J, Nguyen Hieu T M, Zhang Jason, Fox Matthew C, Sebastian Katherine R, Reichenberg Jason S, Markey Mia K, Tunnell James W
Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton Street C0800, Austin, TX 78712, USA.
Medicine, Dell Medical School, The University of Texas at Austin, 1400 N IH-35 Suite C2-470, Austin, TX 78701, USA.
Biomed Opt Express. 2017 May 4;8(6):2835-2850. doi: 10.1364/BOE.8.002835. eCollection 2017 Jun 1.
Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to skin cancer screening data. We expand upon previous models by utilizing skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.
拉曼光谱(RS)在非侵入性癌症筛查中已显示出巨大潜力。基于统计的算法,如主成分分析,通常用于进行组织分类;然而,它们难以与光谱特征和潜在疾病的化学及形态学基础相关联。因此,我们提出了首个应用于皮肤癌筛查数据的拉曼生物物理模型。我们通过将皮肤成分作为构建模块对先前模型进行扩展,并使用从拉曼光纤探头收集的先前临床筛查数据对该模型进行验证。我们构建了一台与共聚焦激光扫描显微镜集成的830nm共聚焦拉曼显微镜。对跨越各种疾病状态的皮肤切片进行拉曼成像,并使用多元曲线分辨(MCR)分析来解析各个皮肤成分的拉曼光谱。将最相关皮肤成分的基础光谱进行线性组合以拟合人体皮肤光谱。我们的结果表明,胶原蛋白、弹性蛋白、角蛋白、细胞核、三油精、神经酰胺、黑色素和水是最重要的模型成分。我们提供了这八种成分的拉曼光谱数据库以供下载(见补充信息),供其他人用作参考。我们的模型揭示了正常、非黑色素瘤和黑色素瘤皮肤癌以及癌前病变的生化和结构组成,为这种非侵入性皮肤癌诊断方法的未来发展铺平了道路。