Tri-modal spectroscopy (TMS) offers a method to minimally-invasively identify changes in tissue biochemistry and morphology in order to distinguish normal, precancerous, and cancerous tissues in vivo. Although TMS has been very successful in several tissue types to date (e.g. cervix and esophagus), new mathematical algorithms used to describe collected reflectance and fluorescence spectra could improve the capability of TMS to describe the disease state of the tissue. We propose to develop and test new data solution algorithms used in TMS to more accurately describe the bilayer tissue structure found in mucosal tissues. We will develop data solution algorithms to describe reflectance spectra in bi-layer tissues (Aim 1). In order to test these algorithms, we will construct bi-layer tissue phantoms with known optical properties (Aim 2). Finally, we will use the new data solution algorithms to determine disease state (i.e. normal, precancerous, or cancerous) in oral cavity based on reflectance and fluorescence spectra collected in vivo (Aim 3).
