Quantitative tool for rapid disease mapping using optical coherence tomography images of azoxymethane-treated mouse colon.

Optical coherence tomography (OCT) can provide new insight into disease progression and therapy by enabling nondestructive, serial imaging of in vivo cancer models. In previous studies, we have shown the utility of endoscopic OCT for identifying adenomas in the azoxymethane-treated mouse model of colorectal cancer and tracking disease progression over time. Because of improved imaging speed made possible through Fourier domain imaging, three-dimensional imaging of the entire mouse colon is possible. Increased amounts of data can facilitate more accurate classification of tissue but require more time on the part of the researcher to sift through and identify relevant data. We present quantitative software for automatically identifying potentially diseased areas that can be used to create a two-dimensional "disease map" from a three-dimensional Fourier domain OCT data set. In addition to sensing inherent changes in tissue that occur during disease development, the algorithm is sensitive to exogeneous highly scattering gold nanoshells that can be targeted to disease biomarkers. The results of the algorithm were compared to histological diagnosis. The algorithm was then used to assess the ability of gold nanoshells targeted to epidermal growth factor receptor in vivo to enable functional OCT imaging.