Discrimination between Nontumor Bladder Tissue and Tumor by Raman Spectroscopy

Abstract

We have applied Raman spectroscopy to discriminate between nontumor and tumor bladder tissue and to determine the biochemical differences therein. Tissue samples from 15 patients were collected, and frozen sections were made for Raman spectroscopy and histology. Twenty-five pseudocolor Raman maps were created in which each color represents a cluster of spectra measured on tissue areas of similar biochemical composition. For each cluster, the cluster-averaged spectrum (CAS) was calculated and classified as tumor and nontumor in accordance to pathohistology. Unguided hierarchical clustering was applied to display heterogeneity between and within groups of nontumor and tumor CAS. A linear discriminant analysis model was developed to discriminate between CAS from tumor and nontumor. The model was tested by a leave-one-patient-out validation, 84 of the 90 CAS (93%) were correctly classified with 94% sensitivity and 92% specificity. Biochemical differences between tumor and nontumor CAS areas were analyzed by fitting spectra of pure compounds to the CAS. Nontumor CAS showed higher collagen content while tumor CAS were characterized by higher lipid, nucleic acid, protein, and glycogen content. Raman spectroscopy enabled effective discrimination between tumor and nontumor bladder tissue based on characterized biochemical differences, despite heterogeneity expressed in both tumor and nontumor CAS.

Label‐free biochemical characterization of stem cells using vibrational spectroscopy

“…This can be a tedious process and it may not be able to extract the more complex, correlated spectral features that best separate the groups. Multivariate statistical methods [29][30][31] have been shown to be powerful approaches for analyzing multidimensional spectral data for the purposes of optimizing group separation and classifying unknown objects to groups. Principal component analysis (PCA) is a popular technique for unsupervised analysis of spectra.…”

Optimal choice of sample substrate and laser wavelength for Raman spectroscopic analysis of biological specimen

“…5). For example, previous studies published by Stone et al, 32 Grimbergen et al 8 and de Jong et al 10 have shown good spectra for samples recorded at 830 nm/845 nm using calcium uoride substrates, which could be achieved here by simply increasing the laser power, enlarging the confocal aperture or increasing the exposure time. The results shown here may also be accounted for by considering the signicantly lower number of scattered photons for higher laser wavelengths (see Section 2.2 on spectral resolution) and the quantum efficiency of the CCD detector used for recording spectra.…”

“…Previous studies have reported the use of a wide range of substrates including calcium uoride, [8][9][10] aluminium, 11,12 quartz 13-15 and 3D collagen gels. 16 However, substrates that produce low background signals for NIR sources are oen expensive, and are available in different levels of purity/lm thicknesses, which can produce variable results with Raman spectroscopy.…”

Detection of Suspicious Tissue Lesions

“…De Jong et al (2006) clearly outlined the prerequisites which must be met to promote RS to the state of clinical in vivo application. First, a database of spectra must be established that is representative of normal and diseased tissue which may be encountered in practice.…”

“…Second, a classification model must be developed, based on this spectral database and histological analysis of the tissue of which spectra were obtained. Third, the accuracy of this model must be tested, and the biochemical basis for the classification model should be investigated [6].…”