Abstract

Biophysical techniques play an important role in detecting physiological alterations during pathogenesis. Raman spectroscopy has shown immense potential in identifying several diseased conditions, including oral cancers. Classification of normal, inflammatory, premalignant and malignant conditions has been demonstrated using ex vivo Raman spectroscopy. Feasibility of recording in vivo spectra in clinically implementable time has also been shown. Translation of this technology to clinics requires extensive validation of methodologies, building of robust models and testing the same under stringent conditions as well as on diverse populations. In this context, the ability of Raman spectroscopy in identifying subtle changes in oral mucosa with increasing age, and the influence of these aging related changes on classification with tobacco-related pathological changes was evaluated. A total of 451 spectra from 62 subjects were recorded from buccal mucosa of healthy subjects of 4 different age groups (aged 20-60 years). Also, 478 spectra from 85 subjects belonging to 4 different categories, tobacco exposed mucosa, contralateral normal (opposite side of tumor), premalignant patches and tumors on buccal mucosa were recorded using fiber optic probe-coupled commercial Raman spectrometer. Differences in spectra were explored by unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA), followed by Leave one out cross validation. Results indicate feasibility of classifying early and late age groups. Also, clear classification is observed between healthy and pathological groups, thus inherent heterogeneity in healthy groups seems to have no bearing on classification of normal with abnormal conditions. Findings of the study indicate high sensitivity of Raman spectroscopy in detecting subtle mucosal changes, further supporting efficacy of Raman spectroscopic approaches in oral cancer applications. Prospectively, more vigorous validation studies of Raman methodology would enable routine clinical applications.

Full Text
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