Understanding the variations in fluorescence spectra of gynecologic tissue

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Abstract

Optical spectroscopy has shown promise as a diagnostic tool for detecting
cervical pre-cancer because spectral variations in optical measurements are
closely correlated with the molecular and architectural changes in tissue that
accompany dysplastic progression. However, optical measurements from cervical
tissue are also affected by other factors, such as age or menopausal status of the
patient. In order to develop robust diagnostic algorithms based on optical
measurements, it is important to identify diagnostically significant features and to
devise methods to extract them from the spectral variations.
Principal component analysis (PCA) is a statistical method of extracting
features based on the variance in a dataset. PCA applied to fluorescence
measurements from cervical tissue revealed biophysically significant spectral
variations during the menstrual cycle. We have also applied PCA in developing a
classification algorithm to discriminate a pair of diagnostic classes.
Although statistical methods can reveal subtle changes in optical spectra
that are diagnostically significant, it is difficult to interpret the biophysical
significance of the extracted features. Another approach is to extract the tissue
optical parameters that are directly related to precancerous changes. In order to
perform model-based parameter estimation, an analytical model was developed to
describe fluorescence in two-layered tissue such as the cervix. Briefly, the model
uses exponential attenuation and diffusion theory, respectively, to describe light
propagation in the epithelium and the stroma, and calculates the total detected
fluorescence as the sum of the fluorescence signals emitted from the two layers.
In the inverse model, the analytical model was iteratively fitted to the measured
fluorescence spectra, and as a result of the fitting process, the optical parameters
are estimated. Validations with Monte Carlo simulations show that optical
properties of the epithelium and the stroma can be estimated accurately. The
inverse model was subsequently applied to a large-scale clinical data, and the
estimated parameters show good correlation with changes associated with
dysplastic progression as well as age.