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1 May 2008 Fluorescence spectroscopy for the detection of tongue carcinoma—validation in an animal model
Cristina Kurachi D.D.S., Carla Raquel Fontana D.D.S., Luiz Eduardo Blumer Rosa, Vanderlei Salvador Bagnato D.D.S.
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Journal of Biomedical Optics, Vol. 13, Issue 3, 034018 (May 2008). https://doi.org/10.1117/1.2937214
Abstract
The efficacy of fluorescence spectroscopy to detect squamous cell carcinoma is evaluated in an animal model following laser excitation at 442 and 532 nm. Lesions are chemically induced with a topical DMBA application at the left lateral tongue of Golden Syrian hamsters. The animals are investigated every 2 weeks after the 4th week of induction until a total of 26 weeks. The right lateral tongue of each animal is considered as a control site (normal contralateral tissue) and the induced lesions are analyzed as a set of points covering the entire clinically detectable area. Based on fluorescence spectral differences, four indices are determined to discriminate normal and carcinoma tissues, based on intraspectral analysis. The spectral data are also analyzed using a multivariate data analysis and the results are compared with histology as the diagnostic gold standard. The best result achieved is for blue excitation using the KNN (K-nearest neighbor, a interspectral analysis) algorithm with a sensitivity of 95.7% and a specificity of 91.6%. These high indices indicate that fluorescence spectroscopy may constitute a fast noninvasive auxiliary tool for diagnostic of cancer within the oral cavity.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
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Cristina Kurachi D.D.S., Carla Raquel Fontana D.D.S., Luiz Eduardo Blumer Rosa, and Vanderlei Salvador Bagnato D.D.S. "Fluorescence spectroscopy for the detection of tongue carcinoma—validation in an animal model," Journal of Biomedical Optics 13(3), 034018 (1 May 2008). https://doi.org/10.1117/1.2937214
Published: 1 May 2008
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Cited by 9 scholarly publications.
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KEYWORDS
Luminescence
Cancer
Tongue
Fluorescence spectroscopy
Diagnostics
Tissues
Tumor growth modeling
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