Understanding the variations in fluorescence spectra of gynecologic tissue

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.