PTH-175 Raman mapping for pathology classification: the need for speed
PTH-175 Raman mapping for pathology classification: the need for speed
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PTH-175 用于病理分类的拉曼图谱：对速度的需求
DOI：
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发表时间：
2015
期刊：
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通讯作者：
N. Stone
中科院分区：
文献类型：
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作者：
O. Old;M. Isabelle;G. Lloyd;C. Kendall;H. Barr;N. Stone
介绍拉曼光谱已被证明可以准确地分类各种条件和器官系统中的组织病理学。这项工作的大部分已经进行了使用拉曼显微光谱仪上离体组织切片与长采集时间，测量可能需要数小时。为了使该技术能够转化为临床环境，无论是作为病理学家的辅助工具还是作为现场检测的体内探针，都必须减少测量时间。通过使用雷尼绍的Streamline™技术，结合了线聚焦技术和像素合并，可以更快地收集拉曼光谱测量，而不会影响信噪比。本研究的目的是评估这种技术的能力，准确地分类组织病理学，使用食管模型。方法收集开放性食管手术患者的食管组织。标本收集自Barrett食管（BO）、异型增生和腺癌患者，并在液氮中快速冷冻。在氟化钙载玻片上制备8 µm组织切片，连续切片用苏木素和伊红（H&E）染色以进行组织学比较。使用Streamline™以1.1 µm的空间分辨率采集60 s/像素的拉曼光谱，在组织病理学的均匀区域中收集拉曼光谱。构建分类模型以区分病理亚型。结果利用先进的多元统计分析工具建立病理分类模型，并采用留一法交叉验证。使用拉曼光谱区分异型增生/腺癌与巴雷特食管的病理学分类模型的灵敏度和特异性分别产生>75%和> 75%的灵敏度和特异性。结论将多元统计分析与StreamlineTM拉曼光谱数据采集相结合，具有良好的灵敏度和特异性。本研究说明了非侵入性快速拉曼光谱映射测量的潜力，并开发了一种稳健且经验证的食管分类模型，该模型能够对组织病理进行分类，为病理学家和临床医生提供临床工具。利益披露无声明。参考文献Shetty G，et al.拉曼光谱：食管癌变过程中的生化变化的阐明。英国癌症杂志。2006;94（10）：1460-4
Introduction Raman spectroscopy has been shown to accurately classify tissue pathology in a variety of conditions and organ systems. Much of this work has been performed using Raman microspectrometers on ex vivotissue sections with long acquisition times, and measurements can take many hours. To enable translation of this technology to a clinical setting, either as an adjunct for pathologists or an in vivoprobe for point of care testing, measurement times must be reduced. By using Renishaw’s Streamline™ technologywhich combines a line focusing technique and pixel binning, it is possible to collect Raman spectral measurements much faster without compromising signal to noise. This study aims to assess the ability of this technique to accurately classify tissue pathology, using an oesophageal model. Method Tissue was collected from the oesophagus in patients undergoing endoscopy of open surgery on the oesophagus. Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8 µm tissue sections were prepared onto calcium fluoride slides, with contiguous sections stained with haematoxylin and eosin (H&E) for histological comparison. Raman spectra were collected across homogeneous regions of tissue pathology, using Streamline™ acquisitions of 60 s per pixel, at 1.1 µm spatial resolution. Classification models were constructed to discriminate pathology subtypes. Results Advanced multivariate statistic analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. The sensitivity and specificity of this pathology classification model using Raman Spectroscopy to discriminate dysplasia/adenocarcinoma from Barrett’s oesophagus produced sensitivity and specificities >75% and >75%, respectively. Conclusion By combining multivariate statistical analysis with StreamlineTM Raman acquisition of spectral data, we have demonstrated good sensitivities and specificities. This study illustrates the potential of non-invasive rapid Raman spectral mapping measurements and development of a robust and validated oesophageal classification model that are able to classify tissue pathology both providing a clinical tool for pathologists and clinicians. Disclosure of interest None Declared. Reference Shetty G, et al. Raman spectroscopy: elucidation of biochemical changes in carcinogenesis of oesophagus. Br J Cancer. 2006;94(10):1460–4