Vibrational Spectroscopy and Cancer - Vibrational Spectroscopy for Tissue Analysis - page 77

with an excitation wavelength of 1064 nm, without any pretreatment.
Principal component analysis (PCA) was performed to distinguish gastric
cancer and nonneoplastic tissue, and discriminant analysis was used to
evaluate the accuracy of the gastric cancer diagnosis. It was reported that
the main difference between the two groups of samples was observed in
the area of 1644 cm −1 . The accuracy of diagnosis using this single peak was
70%, consistent with the PCA result. The overall sensitivity was 66%, and
the specificity was 73%.
The purpose of a research carried out by Teh et al. was to explore near-
infrared (NIR) Raman spectroscopy for identifying precancer (dysplasia)
from normal gastric mucosa tissues [108]. A total of 76 gastric tissue samples
obtained from 44 patients who underwent endoscopy investigation or gas-
trectomy operation were used in this study. The histopathological examina-
tions showed that 55 tissue specimens were normal and 21 showed dysplasia.
Both the empirical approach and multivariate statistical techniques, includ-
ing PCA and LDA, together with the leave-one-sample-out cross-validation
method, were employed to develop effective diagnostic algorithms for clas-
sification of Raman spectra. Raman spectra showed significant differences
between normal and dysplastic tissue, particularly in the spectral ranges of
850-900, 1200-1290, and 1500-1800 cm −1 which contained signals related to
hydroxyproline, amide III and amide I of proteins, and C=C stretching of
lipids, respectively. The empirical diagnostic algorithm based on the ratio
of the Raman peak intensity at 875 cm −1 to the peak intensity at 1450 cm −1
gave diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the
diagnostic algorithms based on PCA-LDA yielded diagnostic sensitivity of
95.2% and specificity 90.9% for separating dysplasia from normal gastric
tissue. It was also demonstrated that the ratio of peak intensities at 875 to
1450 c m −1 provided good differentiation between normal and dysplastic
gastric tissue.
Pandya et al. evaluated the ability of Raman spectroscopy to differentiate
normal pancreatic tissue from malignant tumours in a mouse model [109].
They collected 920 spectra, 475 from 31 normal pancreatic tissue and 445
from 29 tumour nodules using a 785-nm near-infrared laser excitation. Using
principal component analysis, subtle chemical differences in normal and
malignant tissue were successfully highlighted. Using histopathology as the
gold standard, Raman analysis gave sensitivities between 91% and 96% and
specificities between 88% and 96%. In this study, pancreatic tumours were
characterized by increased collagen content and decreased DNA, RNA, and
lipid components compared with normal pancreatic tissue.
Hu et al. carried out a study on classification of normal and malignant
human gastric mucosa tissue with confocal Raman microspectroscopy
[110]. They analysed thirty-two samples from human gastric mucosa tissue,
including 13 normal and 19 malignant tissue samples. Spectra were obtained
by this technique without any sample preparation. Comparing preprocessed
spectra of malignant gastric mucosa tissues with those of counterpart normal