Characterization of malignant brain tumor using elastic light scattering spectroscopy

Jianmin Gong, Ji Yi, Vladimir M. Turzhitsky, Kenji Muro, Xu Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)303-312
Number of pages10
JournalDisease Markers
Issue number6
StatePublished - 2008
Externally publishedYes


  • Artificial neural network
  • Brain cancer
  • Elastic light scattering spectroscopy
  • Glioma
  • Principle component analysis
  • Spectral slope

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Clinical Biochemistry
  • Biochemistry, medical


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