Characterization of Malignant Brain Tumor Using Elastic Light Scattering Spectroscopy. Issue 6 (4th June 2013)

• Record Type: Journal Article
• Authors: Gong, Jianmin; Yi, Ji; Turzhitsky, Vladimir M.; Muro, Kenji; Li, Xu
• Is Part Of: Disease markers. Volume 25:Issue 6(2008)
• Page Start: 303
• Page End: 312
• Publication Date: 2013-06-04
• Subjects: Brain cancer -- glioma -- elastic light scattering spectroscopy -- spectral slope -- principle component analysis -- artificial neural network; Diagnosis -- Periodicals; Biochemical markers -- Periodicals; Pathology -- Periodicals; 616
• DOI: 10.1155/2008/208120
• Languages: English
• ISSNs: 0278-0240

Abstract

Abstract : We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues. ELS spectra were measured from 393 sites on 36 ex vivo tissue specimen obtained from 29 patients. We employed and compared the performances of three methods of spectral classification for tissue characterization, including spectral slope analysis, principle component analysis (PCA), and artificial neural network (ANN) classification. The ANN classifier yielded the best correlation between spectral pattern and histopathological diagnosis, with a typical sensitivity of 80% and specificity of 93% for differentiating tumor from normal brain tissues. We also demonstrate that all three classification methods discriminate between tumor and normal tissue and have the potential to identify and quantitatively characterize tumor-infiltrated brain tissues.