Huang Zhiwei, Lui Harvey, McLean David I, Korbelik Mladen, Zeng Haishan
Cancer Imaging Department, British Columbia Cancer Research Centre, Vancouver, BC, Canada.
Photochem Photobiol. 2005 Sep-Oct;81(5):1219-26. doi: 10.1562/2005-02-24-RA-449.
The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.
本研究评估了包括近红外（NIR）拉曼光谱、NIR自发荧光光谱以及拉曼与NIR自发荧光复合光谱在内的光学光谱技术对恶性肿瘤进行体内检测的诊断能力。为此使用了一种小鼠肿瘤模型，即将BALB/c小鼠的下背部皮下区域植入Meth-A纤维肉瘤细胞。采用快速采集色散型NIR拉曼系统，在785 nm激光激发下对组织进行拉曼和NIR自发荧光光谱测量。在5秒内获取了与小鼠皮肤和肿瘤组织自发荧光背景相关的高质量体内NIR拉曼光谱。使用包括主成分分析（PCA）和线性判别分析（LDA）在内的多变量统计技术，基于光谱特征开发区分肿瘤与正常组织的诊断算法。采用留一法交叉验证方法对肿瘤组织进行光谱分类，并使用受试者工作特征（ROC）曲线进一步评估所得诊断算法的性能。分析了32个体内拉曼、NIR荧光以及拉曼与NIR荧光复合光谱（16个正常样本，16个肿瘤样本）。基于这三个光谱数据集的LDA模型交叉验证得到的分类结果显示，肿瘤识别的诊断敏感性分别为81.3%、93.8%和93.8%；特异性分别为100%、87.5%和100%；总体诊断准确率分别为90.6%、90.6%和96.9%。ROC曲线表明，最有效的诊断算法来自拉曼与NIR自发荧光复合技术。
