论文标题
口腔癌临床前评估中的多模式光学技术：荧光成像和光谱设备
Multimodal Optical Techniques in Pre-Clinical Evaluation of Oral Cancer: Fluorescence Imaging and Spectroscopic Devices
论文作者
论文摘要
目的：口服鳞状细胞癌（OSCC）患者的存活率非常差，可以使用高度敏感，特异性和准确的技术来改善。自荧光和荧光技术非常敏感且在癌症筛查中有用。此外，荧光光谱与人体组织的分子水平直接相关，可以用作癌症检测的定量工具。材料和方法：在这里，我们报告了多模式自动荧光和荧光成像以及基于光谱（MAF-IS）智能手机的系统的开发，以快速和实时口腔癌筛查。在荧光强度，光谱形状和红移方面，荧光 - 自动荧光图像和光谱数据集显示了口腔癌和正常组织的显着变化。结果：在这项研究中，筛选了188例OSCC患者和13例癌前组织（发育不良和纤维化）的68例（33例癌细胞和35例正常）。该研究的主要发现是存在三个峰，即〜636 nm，〜680 nm和〜705 nm，在自动荧光期间，OSCC的强度降低，强度降低。另一个发现是OSCC，发育不良和正常纤维化的荧光光谱法分别为6.59+-4.54 nm，3+-4.78 nm和1.5+-0.5 nm，可以用作实时筛查中的癌症标记物。最后，将基于支持矢量的机器（SVM）分类器用于从正常组织中分类OSCC组织。平均灵敏度，特异性和准确性分别为88.89％，100％和95％。结论：基于自动荧光和基于荧光的成像和光谱法用于对不同口服病变的临床筛查。
Objective: Survival rate of oral squamous cell carcinoma (OSCC) patients is very poor and can be improved using highly sensitive, specific and accurate techniques. Autofluorescence and fluorescence techniques are very sensitive and useful in cancer screening. Furthermore, fluorescence spectroscopy is directly linked with molecular levels of human tissue and can be used as quantitative tool for cancer detection. Materials and Methods: Here, we report development of multi-modal autofluorescence and fluorescence imaging and spectroscopic (MAF-IS) smartphone-based systems for fast and real time oral cancer screening. Fluorescence-autofluorescence images and spectroscopic datasets shows significant change in oral cancer and normal tissue in terms of fluorescence-intensity, spectral-shape, and red-shift respectively. Results: In this study, total 68 samples (33 cancerous and 35 normal) of 18 OSCC patients and 13 patients of precancerous tissues (dysplasia and fibrosis) are screened. Main remarkable finding of the study is presence of three peaks viz ~636 nm, ~680 nm and ~705 nm with decrease in intensity around 450 nm ~ 520 nm in OSCC in case of autofluorescence. Another finding is red shift in fluorescence spectroscopy of OSCC, dysplasia and fibrosis from normal which is 6.59+-4.54 nm, 3+-4.78 nm and 1.5+-0.5 nm respectively and can be used as cancer marker in real-time screening. Finally, support vector machine (SVM) based classifier is applied for classification of OSCC tissue from normal tissue. The average sensitivity, specificity and accuracy are found as 88.89% ,100 %, and 95%, respectively. Conclusion: Autofluorescence and fluorescence-based imaging and spectroscopy is used for pre-clinical screening of different oral lesions.
