Fluorescence spectroscopy contains diagnostic information about the lung biochemistry and morphology, including tissue optical properties and fluorophores. However, the fluorophore information is generally masked by the optical properties of the tissue, which complicates the evaluation of their role in lung-cancer detection. In this work, we have developed a method for extracting the intrinsic fluorescence spectra from the endoscopic measurements of the combined fluorescence and reflectance spectra. Principle components and classification analysis was performed to evaluate the diagnostic potential of the extracted intrinsic fluorescence spectra from in vivo combined fluorescence and reflectance spectral measurements. We evaluated the diagnostic sensitivity and specificity of both the intrinsic fluorescence and the fluorescence spectra. The results showed that the intrinsic fluorescence spectra contain significant diagnostic information that had been masked by the lung optical properties. We have also found that the intrinsic fluorescence has improved the specificity for endobronchial-cancer detection, although with a slight decrease in the detection sensitivity, when compared to the fluorescence spectra. This may indicate that intrinsic fluorescence analysis could be used to improve the diagnostic specificity of fluorescence spectroscopy and imaging.
We present a method for lung cancer detection exploiting reflectance spectra measured in vivo during endoscopic imaging of the lung. The measured reflectance spectra were analyzed using a specially developed light-transport model to obtain quantitative information about cancer-related, physiological, and morphologic changes in the superficial bronchial mucosa layers. The light-transport model allowed us to obtain the absorption coefficient (µa) and further to derive the micro-vascular blood volume fraction in tissue and the tissue blood oxygen saturation. The model also allowed us to obtain the scattering coefficient (µs) and the anisotropy coefficient (g) and further to derive the tissue scattering micro-particle volume fraction and size distribution. The specular component of the reflectance signal and the instrument response were accounted for during the analysis. The method was validated using 100 reflectance spectra measured in vivo in a noncontact fashion from 22 lung patients (50 normal tissue/benign lesion sites and 50 malignant lesion sites). The classification between normal tissue/benign lesions and malignant lesions was further investigated using the derived quantitative parameters and discriminant function analysis. The results demonstrated significant differences between the normal tissue/benign lesions and the malignant lesions in terms of tissue blood volume fraction, blood oxygen saturation, tissue scatterer volume fractions, and size distribution. The results also showed that the malignant lung lesions can be differentiated from normal tissue/benign lesions with both diagnostic sensitivity and specificity of better than 80%.
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