Vibrational Spectroscopy and Cancer - Vibrational Spectroscopy for Tissue Analysis - page 81

Table 4.9
Articles on Tissues Related to the Oral Cavity

Research
Group

Research
Method

Investigated
Tissue or Sample

Effectiveness of
the Technique

Reference
Number

Fukuyama et al. FTIR Oral tissue + 122

Malini et al. Raman Oral tissue + 123

Wu et al. Raman Oral tissue + 124

Majmuder and
Ghosh

Autofluorescence Oral tissue + 125

Lau et al. Raman Nasopharynx + 126

Lau et al. Raman Larynx + 127

Wu et al. FTIR-ATR +

Raman

Oral tissue + 128

Teh et al. Raman Larynx + 129

Wang et al. FTIR Salivary gland + 130

with the purified human collagen and keratin. One half of every tissue speci-
men was measured with FTIR and the other half was investigated histologi-
cally. The data obtained suggested that this technique is applicable to clinical
diagnostics [122].
R. Malini et al. reported on discrimination of normal, inflammatory, pre-
malignant, and malignant oral tissue using Raman spectroscopy. Spectral
profiles of different samples showed pronounced differences. It was demon-
strated that all the four tissue types could be discriminated and diagnosed
correctly. The biochemical differences between normal and pathological
conditions of oral tissue were also discussed [123].
The application of FTIR fibre-optic technique for distinguishing malignant
from normal oral tissues was reported by J. G. Wu et al. According to the
results, the 1745 cm −1 band, which is assigned to the ester group (C=O) vibra-
tion of triglycerides, is a reliable marker that is present in normal tissue but
absent or weak in malignant oral tissues. In addition, other bands such as
C−H stretching and the aide bands are also helpful in distinguishing the two
groups of samples. Raman spectroscopic measurements were in agreement
with results observed from FTIR spectra [124].
The study of S. K. Majmuder and Ghosh concerned the use of a relevance
vector machine (RVM) for optical diagnosis of cancer. It reported the use of
the theory of the RVM for development of a fully probabilistic algorithm for
autofluorescence diagnosis of early-stage cancer of the human oral cavity.
The diagnostic algorithms were developed using in vivo autofluorescence
spectral data acquired from the human oral cavity with a N 2 laser-based
portable fluorimeter. The sensitivity and specificity toward cancer were up
to 91% and 96%, respectively. When implemented on the spectral data of the
uninvolved oral cavity sited from the patients, it yielded a specificity of up
to 91% [125].