Characterization of malignant brain tumor using elastic light scattering spectroscopy

Issue title: Optical Spectroscopic Markers of Cancer

Article type: Research Article

Authors: Gong, Jianmin | Yi, Ji | Turzhitsky, Vladimir M. | Muro, Kenji | Li, Xu^;

Affiliations: Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA | Department of Neurological Surgery, Northwestern University, Chicago, IL 60611, USA | Department of Electrical Engineering and Computer Science, Evanston, IL 60208, USA

Note: [] Corresponding author: Xu Li, E-mail: xuli@northwestern.edu

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.

Keywords: Brain cancer, glioma, elastic light scattering spectroscopy, spectral slope, principle component analysis, artificial neural network

Journal: Disease Markers, vol. 25, no. 6, pp. 303-312, 2008