Biophotonics methods are attractive since they allow for the non-invasive diagnosis of cancer. Experiments
were carried out to investigate the feasibility of detecting early pre-cancer using optical spectroscopy. However,
optimization of instrumentation design parameters remains challenging because of the lack of metrics to evaluate
the performance of certain design parameters. For example, although using angled-collection geometry has been
shown to collect depth sensitive spatial origins, the performance of devices with angled-collection geometries
are not well characterized or quantified. In this study, we use a polarization-sensitive Monte Carlo simulation
(Pol-MC) to aid in the design of instrumentation for the early detection of epithelial cancer. The tissue is
modeled in layers: (0) air outside the tissue, (1) epithelial layer, (2) thin pre-cancer layer of cells, (3) thin
basement membrane, implemented as a thin transparent layer, and (4) the stroma, implemented as a thick layer
of scattering material. We propose a new metric, Target Signal Ratio (TSR), to evaluate the proportion of signal
that is scattered from a target layer, which is the basal/pre-cancer layer. This study is a proof-of-concept for
the application of computational techniques to facilitate instrument design.Biomedical Engineerin
