Fei Baowei, Lu Guolan, Wang Xu, Zhang Hongzheng, Little James V, Magliocca Kelly R, Chen Amy Y
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University.
Proc SPIE Int Soc Opt Eng. 2017 Jan-Feb;10054. doi: 10.1117/12.2249803. Epub 2017 Feb 14.
We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94±6%, sensitivity of 94±6%, and specificity of 95±6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.
我们正在开发用于肿瘤边缘评估的无标记高光谱成像（HSI）技术。HSI数据，即超立方体（x，y，λ），由在不同波长下获取的同一视野的一系列高分辨率图像组成。HSI图像上的每个像素都有一个光谱。我们为HSI数据开发了预处理和分类方法。我们使用HSI数据的光谱特征对癌症组织和良性组织进行分类。我们收集了16例接受头颈（H&N）癌手术的人类患者的手术组织标本。我们从这些标本中获取了HSI、自发荧光图像以及用2-NBDG和普罗黄素标记的荧光图像。数字化组织学切片由一位头颈病理学家进行检查。高光谱成像和分类方法能够区分口腔癌组织和正常组织，平均准确率为90±8%，灵敏度为89±9%，特异性为91±6%。对于甲状腺组织标本，该方法的平均准确率为94±6%，灵敏度为94±6%，特异性为95±6%。高光谱成像在性能上优于自发荧光成像或使用活性染料（2-NBDG或普罗黄素）的荧光成像。这项研究表明，无标记高光谱成像在H&N癌症患者手术组织标本的肿瘤边缘评估方面具有巨大潜力。高光谱成像技术的进一步发展对于其在图像引导手术中的应用是必要的。
