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Title:Intrinsic Spectroscopic Tumor Markers Revealed by Double-Differential Analysis of Near-Infrared Absorption Spectra
Author(s):Kukreti, Shwayta
Doctoral Committee Chair(s):Gratton, Enrico
Department / Program:Biophysics and Computational Biology
Discipline:Biophysics and Computational Biology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Biology, Physiology
Abstract:Near infrared (NIR) optical methods provide contrast based on quantitative functional changes in tissue. NIR techniques have shown to be sensitive to changes in breast physiology from disease by quantifying total hemoglobin (oxyhemoglobin and deoxyhemoglobin), oxygen saturation, fat content, and water content. Tumors display changes in these parameters; however, it is debatable whether they are specific "signatures" of cancer. Thus differential diagnosis (benign vs. malignant) solely based on changes in normal tissue components has been difficult. The question we address is: are there unique spectral differences between the normal and tumor-containing breast tissues besides spectral differences resulting from tissue composition? To this end we have developed a new approach to spectral analysis of broadband near-infrared (NIR, 650-1000nm) spectra. This is a self-referencing double-differential method which requires high spectral resolution. Application of this method to absorption spectra from tumor-containing tissue reveals specific tumor components (STC). The STC are characterized by specific NIR absorption bands. These absorption bands are tumor specific in at least two important ways: (1) spatial specificity---STC absorption bands are localized to the tumor-containing region. We quantify the spectral changes by characterizing the spectral features in an index called the Specific Tumor Component (STC) Index. The STC index identifies the regions of the breast with tumors. (2) lesion-type specificity---STC spectral shape are different for cancer and fibroadenoma lesions. To discriminate benign and malignant lesions we developed a spectral separation method which exploits the entire wavelength region of the STC spectra. We found that by normalizing the amplitude and weighting wavelength regions we could achieve the best discrimination. The spectral separation method separates lesion types. Currently the biochemical origin of the STC spectrum is unknown, however based on spectral regions of absorption near 760, 930, and 980nm, we identify these biomarkers with changes in state or addition of lipid and/or water.
Issue Date:2007
Description:165 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3301173
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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