title: Model-based analysis of reflectance and fluorescence spectra for in vivo detection of cervical dysplasia and cancer.
abstract: 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 8551%; this is comparable to accuracy reported in other studies of optical technologies for detection of cervical cancer and its precursors.
authors.affiliation: Rice University, Department of Chemistry, Houston, Texas 77005, USA.
authors.forename: Crystal
authors.initials: C
authors.lastname: Redden Weber
authors.forename: Richard A
authors.initials: RA
authors.lastname: Schwarz
authors.forename: E Neely
authors.initials: EN
authors.lastname: Atkinson
authors.forename: Dennis D
authors.initials: DD
authors.lastname: Cox
authors.forename: Calum
authors.initials: C
authors.lastname: Macaulay
authors.forename: Michele
authors.initials: M
authors.lastname: Follen
authors.forename: Rebecca
authors.initials: R
authors.lastname: Richards-Kortum
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis*; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis*
chemical_list: D014408:Biomarkers, Tumor