Butte Pramod V, Pikul Brian K, Hever Aviv, Yong William H, Black Keith L, Marcu Laura
University of Southern California, Department of Biomedical Engineering, Los Angeles, California 90089, USA.
J Biomed Opt. 2005 Nov-Dec;10(6):064026. doi: 10.1117/1.2141624.
We investigate the use of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) as an adjunctive tool for the intraoperative rapid evaluation of tumor specimens and delineation of tumor from surrounding normal tissue. Tissue autofluorescence is induced with a pulsed nitrogen laser (337 nm, 1.2 ns) and the intensity decay profiles are recorded in the 370 to 500 nm spectral range with a fast digitizer (0.2 ns resolution). Experiments are conducted on excised specimens (meningioma, dura mater, cerebral cortex) from 26 patients (97 sites). Spectral intensities and time-dependent parameters derived from the time-resolved spectra of each site are used for tissue characterization. A linear discriminant analysis algorithm is used for tissue classification. Our results reveal that meningioma is characterized by unique fluorescence characteristics that enable discrimination of tumor from normal tissue with high sensitivity (>89%) and specificity (100%). The accuracy of classification is found to increase (92.8% cases in the training set and 91.8% in the cross-validated set correctly classified) when parameters from both the spectral and the time domain are used for discrimination. Our findings establish the feasibility of using TR-LIFS as a tool for the identification of meningiomas and enables further development of real-time diagnostic tools for analyzing surgical tissue specimens of meningioma or other brain tumors.
我们研究了时间分辨激光诱导荧光光谱技术(TR-LIFS)作为一种辅助工具,用于术中快速评估肿瘤标本以及区分肿瘤与周围正常组织。用脉冲氮激光器(337nm,1.2ns)诱导组织自发荧光,并使用快速数字化仪(分辨率0.2ns)在370至500nm光谱范围内记录强度衰减曲线。对26例患者(97个部位)的切除标本(脑膜瘤、硬脑膜、大脑皮层)进行了实验。从每个部位的时间分辨光谱中得出的光谱强度和时间相关参数用于组织表征。使用线性判别分析算法进行组织分类。我们的结果表明,脑膜瘤具有独特的荧光特征,能够以高灵敏度(>89%)和特异性(100%)区分肿瘤与正常组织。当使用光谱和时域的参数进行判别时,发现分类准确率会提高(训练集中92.8%的病例和交叉验证集中91.8%的病例被正确分类)。我们的研究结果证实了使用TR-LIFS作为识别脑膜瘤工具的可行性,并能够进一步开发用于分析脑膜瘤或其他脑肿瘤手术组织标本的实时诊断工具。