1 October 2012 In vivo Raman spectroscopic identification of premalignant lesions in oral buccal mucosa
Author Affiliations +
J. of Biomedical Optics, 17(10), 105002 (2012). doi:10.1117/1.JBO.17.10.105002
Cancers of oral cavities are one of the most common malignancies in India and other south-Asian countries. Tobacco habits are the main etiological factors for oral cancer. Identification of premalignant lesions is required for improving survival rates related to oral cancer. Optical spectroscopy methods are projected as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex-vivo tissues. We intend to evaluate potentials of Raman spectroscopy in detecting premalignant conditions. Spectra were recorded from premalignant patches, contralateral normal (opposite to tumor site), and cancerous sites of subjects with oral cancers and also from age-matched healthy subjects with and without tobacco habits. A total of 861 spectra from 104 subjects were recorded using a fiber-optic probe-coupled HE-785 Raman spectrometer. Spectral differences in the 1200- to 1800-cm −1 region were subjected to unsupervised principal component analysis and supervised linear discriminant analysis followed by validation with leave-one-out and an independent test data set. Results suggest that premalignant conditions can be objectively discriminated with both normal and cancerous sites as well as from healthy controls with and without tobacco habits. Findings of the study further support efficacy of Raman spectroscopic approaches in oral-cancer applications.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)
S. P. Singh, Atul Deshmukh, Pankaj Chaturvedi, C. Murali Krishna, "In vivo Raman spectroscopic identification of premalignant lesions in oral buccal mucosa," Journal of Biomedical Optics 17(10), 105002 (1 October 2012). https://doi.org/10.1117/1.JBO.17.10.105002

Raman spectroscopy


Control systems


In vivo imaging


Principal component analysis

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