Optimum wavelength for the differentiation of brain tumor tissue using autofluorescence spectroscopy.
OBJECTIVE
The role of autofluorescence spectroscopy in the detection and staging of benign and malignant brain tumors is being investigated in this study, with an additional aim of determining an optimum excitation wavelength for the spectroscopic identification of brain tumors.


METHODS
The present study involves in-vitro autofluorescence monitoring of different human brain tumor samples to assess their spectroscopic properties. The autofluorescence measurement at four different excitation wavelengths 320, 370, 410, and 470 nm, were carried out for five different brain tumor types: glioma, astrocytoma, meningioma, pituitary adenoma, and schwannoma.


RESULTS
The fluorescence spectra of tumor tissues showed significant differences, both in intensity and in spectral profile, from those of adjacent normal brain tissues at all four excitation wavelengths. The data were then subjected to multivariate statistical analysis and the sensitivities and specificities were calculated for each group. Of the four excitation wavelengths being considered, 470 nm appeared to be the optimal wavelength for detecting tissue fluorescence of brain tumor tissues.


CONCLUSIONS
In conclusion, the spectroscopic luminescence measurements carried out in this study revealed significant differences between tumor tissue and adjacent normal tissue of human brains for all the tumor types tested, except for pituitary adenoma. From the results of this study we conclude that excitation wavelengths ranging from 410-470 nm are most suitable for the detection of brain tumor tissue. Moreover, in this particular study, only excitation at 470 nm indicated that samples we considered to be normal tissue were not normal, and that these were indeed pituitary adenoma tissues. This distinction was not clear at other excitation wavelengths.
Saraswathy
Ariya
A
Department of Optoelectronics, University of Kerala, Kariavattom, Trivandrum, Kerala, India.
Jayasree
R S
RS
Baiju
K V
KV
Gupta
Arun Kumar
AK
Pillai
V P Mahadevan
VP
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001254:Astrocytoma / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D002648:Child; D002675:Child, Preschool; D016002:Discriminant Analysis; D005260:Female; D005910:Glioma / Q000473:pathology; D006801:Humans; D008297:Male; D008579:Meningioma / Q000473:pathology; D008875:Middle Aged; D009367:Neoplasm Staging; D009442:Neurilemmoma / Q000473:pathology; D010911:Pituitary Neoplasms / Q000473:pathology; D025341:Principal Component Analysis; D013050:Spectrometry, Fluorescence / Q000379:methods*