Gong Jianmin, Yi Ji, Turzhitsky Vladimir M, Muro Kenji, Li Xu
Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
Dis Markers. 2008;25(6):303-12. doi: 10.1155/2008/208120.
We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues. ELS spectra were measured from 393 sites on 36 ex vivo tissue specimen obtained from 29 patients. We employed and compared the performances of three methods of spectral classification for tissue characterization, including spectral slope analysis, principle component analysis (PCA), and artificial neural network (ANN) classification. The ANN classifier yielded the best correlation between spectral pattern and histopathological diagnosis, with a typical sensitivity of 80% and specificity of 93% for differentiating tumor from normal brain tissues. We also demonstrate that all three classification methods discriminate between tumor and normal tissue and have the potential to identify and quantitatively characterize tumor-infiltrated brain tissues.
我们报告了一项试点研究，旨在测试弹性光散射（ELS）光谱技术用于表征正常脑组织、肿瘤组织以及肿瘤浸润脑组织。对从29例患者获取的36个离体组织标本上的393个部位进行了ELS光谱测量。我们采用并比较了三种光谱分类方法用于组织表征的性能，包括光谱斜率分析、主成分分析（PCA）和人工神经网络（ANN）分类。ANN分类器在光谱模式与组织病理学诊断之间产生了最佳相关性，在区分肿瘤组织与正常脑组织时，典型的灵敏度为80%，特异性为93%。我们还证明，所有这三种分类方法都能区分肿瘤组织和正常组织，并且有潜力识别和定量表征肿瘤浸润脑组织。
