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1.  Optical Technologies and Molecular Imaging for Cervical Neoplasia: A Program Project Update 
Gender Medicine  2011;9(1 Suppl):S7-S24.
There is an urgent global need for effective and affordable approaches to cervical cancer screening and diagnosis. For developing nations, cervical malignancies remain the leading cause of cancer death in women. This reality is difficult to accept given that these deaths are largely preventable; where cervical screening programs are implemented, cervical cancer deaths decrease dramatically. In the developed world, the challenges with respect to cervical disease stem from high costs and over-treatment. We are presently eleven years into a National Cancer Institute-funded Program Project (P01 CA82710) that is evaluating optical technologies for their applicability to the cervical cancer problem. Our mandate is to create new tools for disease detection and diagnosis that are inexpensive, require minimal expertise to use, are more accurate than existing modalities, and will be feasibly implemented in a variety of clinical settings. Herein, we update the status of this work and explain the long-term goals of this project.
PMCID: PMC3289763  PMID: 21944317
2.  Classifying tissue samples from measurements on cells with within-class tissue sample heterogeneity 
Biostatistics (Oxford, England)  2011;12(4):695-709.
We consider here the problem of classifying a macro-level object based on measurements of embedded (micro-level) observations within each object, for example, classifying a patient based on measurements on a collection of a random number of their cells. Classification problems with this hierarchical, nested structure have not received the same statistical understanding as the general classification problem. Some heuristic approaches have been developed and a few authors have proposed formal statistical models. We focus on the problem where heterogeneity exists between the macro-level objects within a class. We propose a model-based statistical methodology that models the log-odds of the macro-level object belonging to a class using a latent-class variable model to account for this heterogeneity. The latent classes are estimated by clustering the macro-level object density estimates. We apply this method to the detection of patients with cervical neoplasia based on quantitative cytology measurements on cells in a Papanicolaou smear. Quantitative cytology is much cheaper and potentially can take less time than the current standard of care. The results show that the automated quantitative cytology using the proposed method is roughly equivalent to clinical cytopathology and shows significant improvement over a statistical model that does not account for the heterogeneity of the data.
PMCID: PMC3169670  PMID: 21642388
Automating cervical neoplasia screening; Clustering densities; Cumulative log-odds; Functional data clustering; Macro-level classification; Quantitative cytology
3.  Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia: testing a device as an adjunct to colposcopy 
Testing emerging technologies involves the evaluation of biologic plausibility, technical efficacy, clinical effectiveness, patient satisfaction, and cost-effectiveness. The objective of this study was to select an effective classification algorithm for optical spectroscopy as an adjunct to colposcopy and obtain preliminary estimates of its accuracy for the detection of CIN 2 or worse. We recruited 1000 patients from screening and prevention clinics and 850 patients from colposcopy clinics at two comprehensive cancer centers and a community hospital. Optical spectroscopy was performed and 4864 biopsies were obtained from the sites measured, including abnormal and normal colposcopic areas. The gold standard was the histologic report of biopsies, read 2–3 times by histopathologists blinded to the cytologic, histopathologic, and spectroscopic results. We calculated sensitivities, specificities, receiver operating characteristic (ROC) curves, and areas under the ROC curves. We identified a cutpoint for an algorithm based on optical spectroscopy that yielded an estimated sensitivity of 1.00 [95% confidence interval (CI) = 0.92 – 1.00] and an estimated specificity of 0.71 [95% CI = 0.62 – 0.79] in a combined screening and diagnostic population. The positive and negative predictive values were 0.58 and 1.00, respectively. The area under the ROC curve was 0.85 (95% CI 0.81 – 0.89). The per-patient and per-site performance were similar in the diagnostic and poorer in the screening settings. Like colposcopy, the device performs best in a diagnostic population. Alternative statistical approaches demonstrate that the analysis is robust and that spectroscopy works as well as or slightly better than colposcopy for the detection of CIN 2 to cancer.
PMCID: PMC3015005  PMID: 20830707
sensitivity and specificity; diagnosis; early detection of cancer; uterine cervical neoplasms; cervical intraepithelial neoplasia
4.  Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂ 
Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented.
PMCID: PMC2923818  PMID: 20729976
Robust smoothing; Smoothing parameter selection; Robust cross validation; Leave out schemes; Fluorescence spectroscopy
5.  A Bayesian Hierarchical Model for Classification with Selection of Functional Predictors 
Biometrics  2009;66(2):463-473.
In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical pre-cancer. In this paper, we propose a Bayesian hierarchical model which takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in non-functional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An Evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.
PMCID: PMC3042776  PMID: 19508236
Bayesian hierarchical model; Evolutionary Monte Carlo; Functional data classification; Functional predictor selection; Fluorescence spectroscopy
6.  Bayesian Meta-Analysis of Papanicolaou Smear Accuracy 
Gynecologic oncology  2007;107(1 Suppl 1):S133-S137.
To perform a Bayesian analysis of data from a previous meta-analysis of Papanicolaou (Pap) smear accuracy (Fahey et al. Am J Epidemiol 1995; 141:680–689) and compare the results.
We considered two Bayesian models for the same dataset used in the Fahey et al study. Model I was a beta-binomial model which considered the number of true positives and false negatives as independent binomial random variables with probability parameters β (sensitivity) and a (one minus specificity), respectively. We assumed that β and a are independent, each following a beta distribution with exponential priors. Model II considered sensitivity and specificity jointly through a bivariate normal distribution on the logits of the sensitivity and specificity. We performed sensitivity analysis to examine the effect of prior selection on the parameter estimates.
We compared the estimates of average sensitivity and specificity from the Bayesian models with those from Fahey et al.’s SROC approach. Model I produced results similar to those of the SROC approach. Model II produced point estimates higher than those of the SROC approach, although the credible intervals overlapped and were wider. Sensitivity analysis showed that the Bayesian models are somewhat sensitive to the variance of the prior distribution, but their point estimates are more robust than those of the SROC approach.
The Bayesian approach has advantages over the SROC approach in that it accounts for between-study variation and allows for estimating the sensitivity and specificity for a particular trial, taking into consideration the results of other trials, i.e., “borrowing strength” from other trials.
PMCID: PMC2964866  PMID: 17908587
meta-analysis; sensitivity; specificity; Bayesian model; Papanicolaou smear; cervical cancer
7.  Analyzing Single-Molecule Manipulation Experiments 
Single-molecule manipulation studies can provide quantitative information about the physical properties of complex biological molecules without ensemble artifacts obscuring the measurements. We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained in noisy experimental time series. The “noise” comes from multiple sources, e.g. inherent thermal motion, instrument measurement error, etc. The primary focus of this article is a methodology that uses time domain based methods to extract the effective molecular friction from single-molecule pulling data. We studied molecules composed of 8 tandem repeat titin I27 domains, but the modeling approaches have applicability to other single-molecule mechanical studies. The merits and challenges associated with applying such a computational approach to existing single-molecule manipulation data are also discussed.
PMCID: PMC2760430  PMID: 19479747
8.  Repeatability of tissue fluorescence measurements for the detection of cervical intraepithelial neoplasia 
Biomedical Optics Express  2010;1(2):641-657.
We examined intensity and shape differences in 378 repeated spectroscopic measures of the cervix. We examined causes of variability such as presence of precancer or cancer, pathologic tissue type, menopausal status, hormone or oral contraceptive use, and age; as well as technology related variables like generation of device and provider making exam. Age, device generation, and provider were statistically significantly related to intensity differences. Provider and device generation were related to shape differences. We examined the order of measurements and found a decreased intensity in the second measurement due to hemoglobin absorption. 96% of repeat measurements had classification concordance of cervical intraepithelial neoplasia.
PMCID: PMC3018008  PMID: 21258497
(120.0120) Instrumentation, measurement, and metrology; (170.0170) Medical optics and biotechnology; (300.0300) Spectroscopy
9.  Quantifying multiscale noise sources in single-molecule time series 
When analyzing single-molecule data, a low-dimensional set of system observables typically serve as the observational data. We calibrate stochastic dynamical models from time series that record such observables (our focus throughout is on a molecule’s end-to-end distance). Numerical techniques for quantifying noise from multiple time scales in a single trajectory, including experimental instrument and inherent thermal noise, are demonstrated. The techniques are applied to study time series coming from both simulations and experiments associated with the nonequilibrium mechanical unfolding of titin’s I27 domain. The estimated models can be used for several purposes: (1) detect dynamical signatures of “rare events” by analyzing the effective diffusion and force as a function of the monitored observable; (2) quantify the influence that experimentally unobservable conformational degrees of freedom have on the dynamics of the monitored observable; (3) quantitatively compare the inherent thermal noise to other noise sources, e.g. instrument noise, variation induced by conformational heterogeneity, etc.; (4) simulate random quantities associated with repeated experiments; (5) apply pathwise (i.e. trajectory-wise) hypothesis tests to assess the goodness-of-fit of models and even detect conformational transitions in noisy signals. These items are all illustrated with several examples.
PMCID: PMC2682735  PMID: 19072043
10.  Model-based analysis of reflectance and fluorescence spectra for in vivo detection of cervical dysplasia and cancer 
Journal of biomedical optics  2008;13(6):064016.
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.
PMCID: PMC2701358  PMID: 19123662
diffuse reflectance spectroscopy; fluorescence spectroscopy; cancer diagnosis
11.  Design and Preliminary Analysis of a Study to Assess Intra-device and Inter-device Variability of Fluorescence Spectroscopy Instruments for Detecting Cervical Neoplasia 
Gynecologic oncology  2005;99(3 Suppl 1):S98-111.
A study was designed to assess variability between different fluorescence spectroscopy devices. Measurements were made with all combinations of three devices, four probes, and thee sets of standards trays. Additionally, we made three measurements on the same day over two days for the same combination of device, probe, and standards tray to assess reproducibility over a day and across days.
Material and Methods
The devices consisted of light sources, fiber optics, and cameras. We measured thirteen standards and present the data from: the frosted cuvette, water, and rhodamine standards. A preliminary analysis was performed with the data that was wavelength calibrated and background subtracted however the system has not been corrected for systematic intensity variations caused by the devices. Two analyses were performed on the rhodamine, water, and frosted cuvette standards data. The first one is based on first clustering the measurements and then looking for association between the 5 factors (device, probe, standards tray, day, measurement number) using chi-squared tests on the cross tabulation of cluster and factor level. This showed that only device and probe were significant. We then did an analysis of variance to assess the percent variance explained by each factor that was significant from the chi-squared analysis.
The data were remarkably similar across the different combinations of factors. The analysis based on the clusters showed that sometimes devices alone, probes alone, but most often combinations of device and probe caused significant differences in measurements. The analysis showed that time of day, location of device, and standards trays do not vary significantly; whereas the devices and probes account for differences in measurement. We expected this type of significance using unprocessed data since the processing corrects for differences in devices. However, this analysis on raw data is useful to explore what combination of device and probe measurements should be targeted for further investigation. This experiment affirms that online quality control is necessary to obtain the best excitation-emission matrices from optical spectroscopy devices.
The fact that the device and probe are the primary sources of variability indicates that proper correction for the transfer function of the individual devices should make the measurements essentially equivalent.
PMCID: PMC1861808  PMID: 16188298
fluorescence spectroscopy devices; quality assurance; trial design; probe; Fast EEM; standards

Results 1-11 (11)