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首页> 外文会议>Conference on advances in optics for biotechnology, medicine and surgery XV >A BIOPHYSICAL RAMAN SPECTROSCOPIC MODEL FOR NONINVASIVE SCREENING OF SKIN CANCER
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A BIOPHYSICAL RAMAN SPECTROSCOPIC MODEL FOR NONINVASIVE SCREENING OF SKIN CANCER

机译：皮肤癌无侵袭筛查的生物物理拉曼光谱模型

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Raman spectroscopy (RS) is sensitive to the molecular composition of biological tissues. Raman optical fiber-based probes have demonstrated efficacy in noninvasive cancer screening of the skin, breast, stomach, cervical, lung and brain. Currently, statistical algorithms such as principle component analysis (PCA) are the standard approaches for describing the spectral variance of the RS data and providing tissue classification. However, a PCA-based analysis does not allow for an examination of the biophysical basis of disease, such as microstructural organization of proteins and lipids. Understanding those biophysical parameters is essential to interpret the diagnostic result similar to that a pathologist is familiar reading, and develop diagnostic algorithms for fast and accurate cancer screening. Here, we proposed the first biophysical Raman model for in vivo human skin cancer screening. The basic principle is to describe the in vivo human skin spectra as a linear combination of the basis spectra of the most relevant skin constituents. The fit parameters of the skin constituents provided insights into the biochemical and structural makeup of the skin tissue and were then used to develop diagnostic models to discriminate skin cancers. We expanded upon a previous skin cancer model [1] by using in situ skin constituents as the building blocks instead of synthetic chemicals to better represent the skin microenvironment. To achieve this goal, we performed Raman imaging on human skin sections using a custom built 830nm confocal Raman microscope (Figure 1) and collected a library of basis spectra for individual skin constituents. We then validated our model using a previous clinical screening study [2] that covered a wide range of nonmelanoma and melanoma skin disease states. Our model revealed the most relevant building blocks to represent the human skin RS data: collagen, elastin, keratin, triolein, ceramide, cell nucleus, melanin and water. More importantly, we found collagen and triolein were the most significant biomarkers in discriminating malignant melanoma from benign pigmented lesions, and discriminating nonmelanoma skin cancer from normal skin. In the long term, our biophysical Raman model has the potential to impact the screening and margin assessment in other cancer types, such as breast, colorectal and prostate.
机译：拉曼光谱（RS）对生物组织的分子组成敏感。拉曼光纤的探针在皮肤，乳腺癌，胃，宫颈癌，肺癌和大脑的非侵入性癌症筛查中表现出疗效。目前，诸如原理分量分析（PCA）的统计算法是用于描述RS数据的光谱方差和提供组织分类的标准方法。然而，基于PCA的分析不允许检查疾病的生物物理学基础，例如蛋白质和脂质的微观结构组织。理解这些生物物理参数对于解释与病理学家熟悉的读数类似的诊断结果至关重要，并开发用于快速和准确的癌症筛查的诊断算法。在这里，我们提出了用于体内人体皮肤癌筛选的第一生物物理拉曼模型。基本原理是将体内人体皮肤光谱描述为最相关皮肤成分的基础光谱的线性组合。皮肤成分的配合参数提供了对皮肤组织的生化和结构化妆的见解，然后用于开发诊断模型以区分皮肤癌症。我们通过以原位皮肤成分作为构建块而不是合成化学品来扩展到先前的皮肤癌模型[1]，以更好地代表皮肤微环境。为了实现这一目标，我们使用定制的830nm共焦拉曼显微镜（图1）对人体皮肤部分进行了拉曼成像，并收集了个体皮肤成分的基础光谱库。然后，我们使用先前的临床筛查研究验证了我们的模型[2]，涵盖了各种非曼洲和黑素瘤皮肤病状态。我们的模型揭示了最相关的构建块，代表人体皮肤RS数据：胶原蛋白，弹性蛋白，角蛋白，三重素，神经酰胺，细胞核，黑色素和水。更重要的是，我们发现胶原蛋白和三合油是最重要的生物标志物，以鉴别来自良性着色病变的恶性黑色素瘤，并鉴别来自正常皮肤的非茂隆皮肤癌。从长远来看，我们的生物物理拉曼模型有可能影响其他癌症类型的筛查和保证金评估，例如乳腺，结直肠和前列腺。

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