Optical spectroscopy has shown potential as a tool for precancer detection by discriminating alterations in the optical properties within epithelial tissues. Identifying depth-dependent alterations associated with the progression of epithelial cancerous lesions can be especially challenging in the oral cavity due to the variable thickness of the epithelium and the presence of keratinization. Optical spectroscopy of epithelial tissue with improved depth resolution would greatly assist in the isolation of optical properties associated with cancer progression. Here, we report a fiber optic probe for oblique polarized reflectance spectroscopy (OPRS) that is capable of depth sensitive detection by combining the following three approaches: multiple beveled fibers, oblique collection geometry, and polarization gating. We analyze how probe design parameters are related to improvements in collection efficiency of scattered photons from superficial tissue layers and to increased depth discrimination within epithelium. We have demonstrated that obliquely-oriented collection fibers increase both depth selectivity and collection efficiency of scattering signal. Currently, we evaluate this technology in a clinical trial of patients presenting lesions suspicious for dysplasia or carcinoma in the oral cavity. We use depth sensitive spectroscopic data to develop automated algorithms for analysis of morphological and architectural changes in the context of the multilayer oral epithelial tissue. Our initial results show that OPRS has the potential to improve the detection and monitoring of epithelial precancers in the oral cavity.
We present an approach to adaptively adjust the spectral window sizes for optical spectra feature extraction. Previous studies extracted features from spectral windows of a fixed width. In our algorithm, piecewise linear regression is used to adaptively adjust the window sizes to find the maximum window size with reasonable linear fit with the spectrum. This adaptive windowing technique ensures the signal linearity in defined windows; hence, the adaptive windowing technique retains more diagnostic information while using fewer windows. This method was tested on a data set of diffuse reflectance spectra of oral mucosa lesions. Eight features were extracted from each window. We performed classifications using linear discriminant analysis with cross-validation. Using windowing techniques results in better classification performance than not using windowing. The area under the receiver-operating-characteristics curve for windowing techniques was greater than a nonwindowing technique for both normal versus mild dysplasia (MD) plus severe high-grade dysplasia or carcinama (SD) (MD+SD) and benign versus MD+SD. Although adaptive and fixed-size windowing perform similarly, adaptive windowing utilizes significantly fewer windows than fixed-size windows (number of windows per spectrum: 8 versus 16). Because adaptive windows retain most diagnostic information while reducing the number of windows needed for feature extraction, our results suggest that it isolates unique diagnostic features in optical spectra.
We report the results of an oral cavity pilot clinical trial to detect early precancer and cancer using a fiber optic probe with obliquely oriented collection fibers that preferentially probe local tissue morphology and heterogeneity using oblique polarized reflectance spectroscopy (OPRS). We extract epithelial cell nuclear sizes and 10 spectral features. These features are analyzed independently and in combination to assess the best metrics for separation of diagnostic classes. Without stratifying the data according to anatomical location or level of keratinization, OPRS is found to be sensitive to four diagnostic categories: normal, benign, mild dysplasia, high-grade dysplasia, and carcinoma. Using linear discriminant analysis, separation of normal from high-grade dysplasia and carcinoma yield a sensitivity and specificity of 90 and 86%, respectively. Discrimination of morphologically similar lesions such as normal from mild dysplasia is achieved with a sensitivity of 75% and specificity of 73%. Separation of visually indistinguishable benign lesions from high-grade dysplasia and carcinoma is achieved with good sensitivity (100%) and specificity (85%), while separation of benign from mild dysplasia gives a sensitivity of 92% and a specificity of 69%. These promising results suggest that OPRS has the potential to aid screening and diagnosis of oral precancer and cancer.
We propose an approach to adaptively adjust the spectral window size used to extract features from optical
spectra. Previous studies have employed spectral features extracted by dividing the spectra into several spectral
windows of a fixed width. However, the choice of spectral window size was arbitrary. We hypothesize that
by adaptively adjusting the spectral window sizes, the trends in the data will be captured more accurately.
Our method was tested on a diffuse reflectance spectroscopy dataset obtained in a study of oblique polarization
reflectance spectroscopy of oral mucosa lesions. The diagnostic task is to classify lesions into one of four
histopathology groups: normal, benign, mild dysplasia, or severe dysplasia (including carcinoma). Nine features
were extracted from each of the spectral windows. We computed the area (AUC) under Receiver Operating
Characteristic curve to select the most discriminatory wavelength intervals. We performed pairwise classifications
using Linear Discriminant Analysis (LDA) with leave-one-out cross validation. The results showed that for
discriminating benign lesions from mild or severe dysplasia, the adaptive spectral window size features achieved
AUC of 0.84, while a fixed spectral window size of 20 nm had AUC of 0.71, and an AUC of 0.64 is achieved
with a large window size containing all wavelengths. The AUCs of all feature combinations were also calculated.
These results suggest that the new adaptive spectral window size method effectively extracts features that enable
accurate classification of oral mucosa lesions.
Hyperspectral imaging provides complex image data with spectral information from many fluorescent species contained within the sample such as the fluorescent labels and cellular or pigment autofluorescence. To maximize the utility of this spectral imaging technique it is necessary to couple hyperspectral imaging with sophisticated multivariate analysis methods to extract meaningful relationships from the overlapped spectra. Many commonly employed multivariate analysis techniques require the identity of the emission spectra of each component to be known or pure component pixels within the image, a condition rarely met in biological samples. Multivariate curve resolution (MCR) has proven extremely useful for analyzing hyperspectral and multispectral images of biological specimens because it can operate with little or no a priori information about the emitting species, making it appropriate for interrogating samples containing autofluorescence and unanticipated contaminating fluorescence. To demonstrate the unique ability of our hyperspectral imaging system coupled with MCR analysis techniques we will analyze hyperspectral images of four-color in-situ hybridized rat brain tissue containing 455 spectral pixels from 550 - 850 nm. Even though there were only four colors imparted onto the tissue in this case, analysis revealed seven fluorescent species, including contributions from cellular autofluorescence and the tissue mounting media. Spectral image analysis will be presented along with a detailed discussion of the origin of the fluorescence and specific illustrations of the adverse effects of ignoring these additional fluorescent species in a traditional microscopy experiment and a hyperspectral imaging system.
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