Bourbousson Manon, Soomro Irshad, Baldwin David, Notingher Ioan
University of Nottingham, School of Physics and Astronomy, Nottingham, United Kingdom.
Nottingham University Hospitals NHS Trust, Histopathology Department, Nottingham, United Kingdom.
J Med Imaging (Bellingham). 2019 Jul;6(3):036001. doi: 10.1117/1.JMI.6.3.036001. Epub 2019 Aug 9.
Raman spectroscopy mapping was used to study fresh lung tissues and compare to histology sections. The Raman mapping measurements revealed differences in the molecular composition of normal lung tissue, adenocarcinoma, and squamous cell carcinoma (SCC). Molecular heterogeneity of the tissue samples was well captured by the -means clustering analysis of the Raman datasets, as confirmed by the correlation with the adjacent haematoxylin and eosin (H&E) stained tissue sections. The results indicate that the fluorescence background varies considerably even in samples that appear structurally uniform in the H&E images, both for normal and tumor tissue. The results show that characteristic Raman bands can be used to discriminate between tumorous and nontumorous lung tissues and between adenocarcinoma and SCC tissues. These results indicate the potential to develop Raman classifications models for lung tissues based on the Raman spectral differences at the microscopic level, which can be used for tissue diagnosis or treatment stratification.
拉曼光谱成像技术被用于研究新鲜肺组织，并与组织学切片进行比较。拉曼成像测量揭示了正常肺组织、腺癌和鳞状细胞癌（SCC）在分子组成上的差异。通过对拉曼数据集进行K均值聚类分析，很好地捕捉到了组织样本的分子异质性，这一点通过与相邻苏木精和伊红（H&E）染色组织切片的相关性得到了证实。结果表明，即使在H&E图像中结构上看起来均匀的样本中，无论是正常组织还是肿瘤组织，荧光背景都有很大差异。结果显示，特征拉曼谱带可用于区分肿瘤性和非肿瘤性肺组织，以及腺癌和SCC组织。这些结果表明，基于微观层面的拉曼光谱差异开发肺组织拉曼分类模型具有潜力，可用于组织诊断或治疗分层。
