Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model.
Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model.
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DOI：
10.1364/boe.7.002000
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发表时间：
2016-05-01
影响因子：
3.4
通讯作者：
Jo JA
中科院分区：
文献类型：
--
作者：
Pande P;Shrestha S;Park J;Gimenez-Conti I;Brandon J;Applegate BE;Jo JA
众所周知，口腔癌的进展伴随着组织生物化学和形态学的变化。因此，结合功能和结构成像模式的多模式成像方法可以提供更全面的口腔癌预后。这个想法形成了当前研究的中心主题，其中这个前提是在结合荧光寿命成像（FLIM）和光学相干断层扫描（OCT）的多模态成像系统的背景下进行检查。为此，在本研究的第一部分中，通过使用荧光强度和寿命信息获得的诊断优势进行评估。在研究的第二部分中，FLIM衍生的生化特征的诊断潜力与OCT衍生的形态特征进行了比较。为了进行客观评估，使用信号和图像处理技术分别从FLIM和OCT数据中获得了几个定量生化和形态特征。这些特征随后被用于统计分类框架中，以量化不同特征的诊断潜力。组合FLIM和OCT特征的分类准确度估计为87.4%，在统计学上高于仅基于FLIM（83.2%）或OCT（81.0%）特征的准确度。此外，FLIM和OCT特征提供的补充信息导致组合FLIM和OCT特征用于区分良性的最高灵敏度和特异性（88.2%灵敏度，92.0%质量标准），癌前病变（81.5%敏感，96.0%质量标准），和癌性（90.1%感测，92.0%质量标准）班
It is known that the progression of oral cancer is accompanied by changes in both tissue biochemistry and morphology. A multimodal imaging approach combining functional and structural imaging modalities could therefore provide a more comprehensive prognosis of oral cancer. This idea forms the central theme of the current study, wherein this premise is examined in the context of a multimodal imaging system that combines fluorescence lifetime imaging (FLIM) and optical coherence tomography (OCT). Towards this end, in the first part of the present study, the diagnostic advantage obtained by using both fluorescence intensity and lifetime information is assessed. In the second part of the study, the diagnostic potential of FLIM-derived biochemical features is compared with that of OCT-derived morphological features. For an objective assessment, several quantitative biochemical and morphological features from FLIM and OCT data, respectively, were obtained using signal and image processing techniques. These features were subsequently used in a statistical classification framework to quantify the diagnostic potential of different features. The classification accuracy for combined FLIM and OCT features was estimated to be 87.4%, which was statistically higher than accuracy based on only FLIM (83.2%) or OCT (81.0%) features. Moreover, the complimentary information provided by FLIM and OCT features, resulted in highest sensitivity and specificity for the combined FLIM and OCT features for discriminating benign (88.2% sens., 92.0% spec.), pre-cancerous (81.5% sens., 96.0% spec.), and cancerous (90.1% sens., 92.0% spec.) classes.