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|Title:||Fluorescence spectroscopy to discriminate neoplastic human brain lesions: a study using the spectral intensity ratio and multivariate linear discriminant analysis.|
|Citation:||Laser Physics. 2014;24(2):025602|
|Abstract:||Fluorescence spectroscopy is an emerging tool used to differentiate normal and malignant tissue based on the emission spectral profile from endogenous fluorophores. The goal of this study is to estimate the concentration of fluorophores using autofluorescence spectroscopy and try to utilize its diagnostic potential on samples of clinical importance. Brain tumor tissues from patients who received craniotomy for the removal of astrocytoma, glioma, meningioma and schwannoma were utilized in this study. Fluorescence emissions of the formalin fixed samples were recorded at excitation wavelengths of 320 and 410 nm. The emission characteristics of fluorophores such as collagen, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), phospholipids and porphyrins of tumor tissue and adjacent normal tissue were elicited. Exact tissue classification was carried out using the spectral intensity ratio (SIR) and multivariate principal component analysis–linear discriminant analysis (PCA–LDA). The diagnostic algorithm based on PCA–LDA provided better classification efficiency than SIR. Moreover, the spectral data based on an excitation wavelength of 410 nm are found to be more efficient in the classification than 320 nm excitation, using PCA–LDA. Better efficacy of PCA–LDA in tissue classification was further confirmed by the receiver operator characteristic (ROC) curve method. The results of this study establish the feasibility of using fluorescence spectroscopy based real time tools for the discrimination of brain tumors from the adjacent normal tissue during craniotomies, which at present faces a huge challenge.|
|Appears in Collections:||Journal Articles|
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