A Monte Carlo model with site-specific input is used to predict depth-resolved fluorescence spectra from individual normal, inflammatory, and neoplastic oral sites. Our goal in developing this model is to provide a computational tool to study how the morphological characteristics of the tissue affect clinically measured spectra. Tissue samples from the measured sites are imaged using fluorescence confocal microscopy; autofluorescence patterns are measured as a function of depth and tissue sublayer for each individual site. These fluorescence distributions are used as input to the Monte Carlo model to generate predictions of fluorescence spectra, which are compared to clinically measured spectra on a site-by-site basis. A lower fluorescence intensity and longer peak emission wavelength observed in clinical spectra from dysplastic and cancerous sites are found to be associated with a decrease in measured fluorescence originating from the stroma or deeper fibrous regions, and an increase in the measured fraction of photons originating from the epithelium or superficial tissue layers. The simulation approach described here can be used to suggest an optical probe design that samples fluorescence at a depth that gives optimal separation in the spectral signal measured for benign, dysplastic, and cancerous oral mucosa.
We present a Monte Carlo model to predict fluorescence spectra of the oral mucosa obtained with a depth-selective fiber optic probe as a function of tissue optical properties. A model sensitivity analysis determines how variations in optical parameters associated with neoplastic development influence the intensity and shape of spectra, and elucidates the biological basis for differences in spectra from normal and premalignant oral sites. Predictions indicate that spectra of oral mucosa collected with a depth-selective probe are affected by variations in epithelial optical properties, and to a lesser extent, by changes in superficial stromal parameters, but not by changes in the optical properties of deeper stroma. The depth selective probe offers enhanced detection of epithelial fluorescence, with 90% of the detected signal originating from the epithelium and superficial stroma. Predicted depth-selective spectra are in good agreement with measured average spectra from normal and dysplastic oral sites. Changes in parameters associated with dysplastic progression lead to a decreased fluorescence intensity and a shift of the spectra to longer emission wavelengths. Decreased fluorescence is due to a drop in detected stromal photons, whereas the shift of spectral shape is attributed to an increased fraction of detected photons arising in the epithelium.
KEYWORDS: Reflectivity, Luminescence, Tissues, Data modeling, Tissue optics, Optical properties, In vivo imaging, Scattering, Monte Carlo methods, Model-based design
Development, validation, and implementation of an analytical model to extract biologically and diagnostically relevant parameters from measured cervical tissue reflectance and fluorescence spectra are presented. Monte Carlo simulations of tissue reflectance are used to determine the relative contribution of the signal from the epithelium and stroma. The results indicate that the clinical probe used collects a majority of its reflectance signal from the stroma; therefore, a one-layer analytical model of reflectance is used. Two analytical approaches to calculate reflectance spectra are compared to Monte Carlo simulations, and a diffusion theory-based model is implemented. The model is validated by fitting spectra generated from Monte Carlo simulations and comparing the input and output parameters. Median agreement between extracted optical properties and input parameters is 10.6%. The reflectance model is used together with an analytical model of tissue fluorescence to extract optical properties and fluorophore concentrations from 748 clinical measurements of cervical tissue. A diagnostic algorithm based on these extracted parameters is developed and evaluated using cross-validation. The sensitivity/specificity of this algorithm relative to the gold standard of histopathology per measurement are 85/51%; this is comparable to accuracy reported in other studies of optical technologies for detection of cervical cancer and its precursors.
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