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Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model

机译:自动分析多模态荧光寿命成像和光学相干断层扫描数据以诊断仓鼠脸颊袋模型中的口腔癌

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摘要

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.
机译:已知口腔癌的进展伴随组织生物化学和形态的变化。因此,结合功能性和结构性成像方式的多模式成像方法可以为口腔癌提供更全面的预后。这个想法构成了当前研究的中心主题,其中在结合荧光寿命成像(FLIM)和光学相干断层扫描(OCT)的多峰成像系统的背景下检验了这一前提。为此,在本研究的第一部分中,评估了通过同时使用荧光强度和寿命信息获得的诊断优势。在研究的第二部分中,将FLIM衍生的生化特征与OCT衍生的形态学特征的诊断潜力进行了比较。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数据中获得了一些定量的生化和形态特征。这些功能随后用于统计分类框架中,以量化不同功能的诊断潜力。 FLIM和OCT组合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,由FLIM和OCT功能提供的补充信息可导致对FLIM和OCT组合功能的最高敏感性和特异性,以区分良性(88.2%,92.0%规格),癌前(81.5%,96.0)。规格)和癌性(90.1%感度,92.0%规格)类别。

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