Krafft Christoph, Sobottka Stephan B, Geiger Kathrin D, Schackert Gabriele, Salzer Reiner
Institute for Analytical Chemistry, Dresden University of Technology, 01062 Dresden, Germany.
Anal Bioanal Chem. 2007 Mar;387(5):1669-77. doi: 10.1007/s00216-006-0892-5. Epub 2006 Nov 14.
Infrared (IR) spectroscopy provides a sensitive molecular fingerprint for tissue without external markers. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. Infrared imaging spectrometers equipped with multi-channel detectors combine the spectral and spatial information. Tissue areas of 4 x 4 mm(2) can be analyzed within a few minutes in the macroscopic imaging mode. An approach is described to apply this methodology to human astrocytic gliomas, which are graded according to their malignancy from one to four. Multiple IR images of three tissue sections from one patient with a malignant glioma are acquired and assigned to the six classes normal brain tissue, astrocytoma grade II, astrocytoma grade III, glioblastoma multiforme grade IV, hemorrhage, and other tissue by a linear discriminant analysis model which was trained by data from a single-channel detector. Before the model is applied here, the spectra are shown to be virtually identical. The first specimen contained approximately 95% malignant glioma regions, that means astrocytoma grade III or glioblastoma. The smaller percentage of 12-34% malignant glioma in the second specimen is consistent with its location at the tumor periphery. The detection of less than 0.2% malignant glioma in the third specimen points to a location outside the tumor. The results were correlated with the cellularity of the tissue which was obtained from the histopathologic gold standard. Potential applications of IR spectroscopic imaging as a rapid tool to complement established diagnostic methods are discussed.
红外（IR）光谱可为无外部标记的组织提供灵敏的分子指纹图谱。可训练监督分类模型根据光谱指纹图谱识别组织类型。配备多通道探测器的红外成像光谱仪可结合光谱和空间信息。在宏观成像模式下，几分钟内即可分析4×4 mm²的组织区域。本文描述了一种将该方法应用于人类星形胶质细胞瘤的方法，这些肿瘤根据恶性程度从一级到四级进行分级。获取了一名恶性胶质瘤患者三个组织切片的多个红外图像，并通过由单通道探测器数据训练的线性判别分析模型将其分为六类：正常脑组织、二级星形细胞瘤、三级星形细胞瘤、四级多形性胶质母细胞瘤、出血和其他组织。在本文应用该模型之前，已证明光谱几乎相同。第一个样本中约95%为恶性胶质瘤区域，即三级星形细胞瘤或胶质母细胞瘤。第二个样本中恶性胶质瘤的比例较小，为12 - 34%，与其位于肿瘤周边的位置一致。第三个样本中恶性胶质瘤的检测率低于0.2%，表明其位于肿瘤外部。结果与通过组织病理学金标准获得的组织细胞密度相关。本文还讨论了红外光谱成像作为一种快速工具补充现有诊断方法的潜在应用。
