Use of Raman spectroscopy to evaluate the biochemical composition of normal and tumoral human brain tissues for diagnosis.

Raman spectroscopy was used to identify biochemical differences in normal brain tissue (cerebellum and meninges) compared to tumors (glioblastoma, medulloblastoma, schwannoma, and meningioma) through biochemical information obtained from the samples. A total of 263 spectra were obtained from fragments of the normal cerebellum (65), normal meninges (69), glioblastoma (28), schwannoma (8), medulloblastoma (19), and meningioma (74), which were collected using the dispersive Raman spectrometer (830 nm, near infrared, output power of 350 mW, 20 s exposure time to obtain the spectra), coupled to a Raman probe. A spectral model based on least squares fitting was developed to estimate the biochemical concentration of 16 biochemical compounds present in brain tissue, among those that most characterized brain tissue spectra, such as linolenic acid, triolein, cholesterol, sphingomyelin, phosphatidylcholine, ß-carotene, collagen, phenylalanine, DNA, glucose, and blood. From the biochemical information, the classification of the spectra in the normal and tumor groups was conducted according to the type of brain tumor and corresponding normal tissue. The classification used in discrimination models were (a) the concentrations of the biochemical constituents of the brain, through linear discriminant analysis (LDA), and (b) the tissue spectra, through the discrimination by partial least squares (PLS-DA) regression. The models obtained 93.3% discrimination accuracy through the LDA between the normal and tumor groups of the cerebellum separated according to the concentration of biochemical constituents and 94.1% in the discrimination by PLS-DA using the whole spectrum. The results obtained demonstrated that the Raman technique is a promising tool to differentiate concentrations of biochemical compounds present in brain tissues, both normal and tumor. The concentrations estimated by the biochemical model and all the information contained in the Raman spectra were both able to classify the pathological groups.

Discriminating neoplastic and normal brain tissues in vitro through Raman spectroscopy: a principal components analysis classification model.

BACKGROUND AND OBJECTIVE: Because of their aggressiveness, brain tumors can lead to death within a short time after diagnosis. Optical techniques such as Raman spectroscopy may be a technique of choice for in situ tumor diagnosis, with potential use in determining tumor margins during surgery because of its ability to identify biochemical changes between normal and tumor brain tissues quickly and without tissue destruction. METHODS: In this work, fragments of brain tumor (glioblastoma, medulloblastoma, and meningioma) and normal tissues (cerebellum and meninges) were obtained from excisional intracranial surgery and from autopsies, respectively. Raman spectra (dispersive spectrometer, 830 nm 350 mW, 50 sec accumulation, total 172 spectra) were obtained in vitro on these fragments. It has been developed as a model to discriminate between the spectra of normal tissue and tumors based on the scores of principal component analysis (PCA) and Euclidean distance. RESULTS: ANOVA indicated that the scores of PC2 and PC3 show differences between normal and tumor groups (p<0.05) which could be employed in a discrimination model. PC2 was able to discriminate glioblastoma from the other tumors and from normal tissues, showing featured peaks of lipids/phospholipids and cholesterol. PC3 discriminated medulloblastoma and meningioma from normal tissues, with the most intense spectral features of proteins. PC3 also discriminated normal tissues (meninges and cerebellum) by the presence of cholesterol peaks. Results indicated a sensitivity and specificity of 97.4% and 100%, respectively, for this in vitro diagnosis of brain tumor. CONCLUSIONS: The PCA/Euclidean distance model was effective in differentiating tumor from normal spectra, regardless of the type of tissue (meninges or cerebellum).