Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections
Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections
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DOI：
10.1007/s11060-013-1326-9
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发表时间：
2014-02-01
影响因子：
3.9
通讯作者：
Auner, Gregory W.
中科院分区：
文献类型：
--
作者：
Kalkanis, Steven N.;Kast, Rachel E.;Auner, Gregory W.
需要一种高度准确、有效且廉价的工具来在手术室中快速、实时地区分正常脑组织与多形性胶质母细胞瘤（GBM）和坏死边界。拉曼光谱提供了组织类型的独特生物化学特征，具有提供肿瘤和坏死边界的术中识别的潜力。我们的目的是开发一个数据库的拉曼光谱从正常的大脑，GBM，和坏死，并区分这些病理的方法。使用拉曼光谱法使用785 nm激发波长测量来自40个冷冻组织切片的95个区域。对相邻苏木精和伊红切片进行审查，证实了每个区域的组织学。选择正常灰质、坏死和GBM的三个区域作为训练集。选择10个区域作为验证集，包含冷冻伪影的组织区域的二次验证集。灰质含有较高的脂质（1061，1081 cm（-1））含量，而坏死显示蛋白质和核酸含量增加（1003，1206，1239，1255-1266，1552 cm（-1））。GBM介于这两个极端之间。判别函数分析显示，在训练、验证和使用冷冻伪影数据集的验证中，区分组织类型的准确率分别为99.6%、97.8%和77.5%。冷冻伪影组的分类降低是由于组织制备损伤。这项研究显示了拉曼光谱作为一种工具，以准确地识别正常的大脑，坏死，GBM，以增强病理诊断的潜力。未来的工作将开发弥漫性胶质瘤和肿瘤边缘的映射图像，以开发术中手术工具。
The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm(-1)) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm(-1)). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 % accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.