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Optical biopsy has been shown to discriminate between normal and diseased tissue with high sensitivity and specificity. Fiber-optic probe-based spectroscopy systems do not provide the necessary spatial information to guide therapy effectively, ultimately requiring a transition from probe-based spectroscopy to spectral imaging. The effect of such a transition on fluorescence and diffuse reflectance line shape is investigated. Inherent differences in spectral line shape between spectroscopy and imaging are characterized and many of these differences may be attributed to a shift in illumination-collection geometry between the two systems. Sensitivity of the line-shape disparity is characterized with respect to changes in sample absorption and scattering as well as to changes in various parameters of the fiber-optic probe design (e.g., fiber diameter, beam steering). Differences in spectral line shape are described in terms of the relative relationship between the light diffusion within the tissue and the distribution of source-detector separation distances for the probe-based and imaging illumination-collection geometries. Monte Carlo simulation is used to determine fiber configurations that minimize the line-shape disparity between the two systems. In conclusion, we predict that fiber-optic probe designs that mimic a spectral imaging geometry and spectral imaging systems designed to emulate a probe-based geometry will be difficult to implement, pointing toward a posteriori correction for illumination-collection geometry to reconcile imaging and probe-based spectral line shapes or independent evaluation of tissue discrimination accuracy for probe-based and spectral imaging systems. OCIS codes: 170.6510, 260.2510, 300.6170, 110.7050.

Complete characterization of a layered tissue requires probing both the biochemical and the morphological information from its different layers at various depths. We report the development of a combined Raman spectroscopy (RS) and optical coherence tomography (OCT) system that is capable of measuring depth-sensitive Raman signal from the tissue layers imaged by the OCT. The sample arm of a real-time time-domain OCT system was modified to allow for co-alignment of the OCT with the Raman probe beam. The depth sensitivity of Raman was obtained by incorporating confocal Raman configuration that minimized out-of-focus Raman scattered light. The system was first validated using a layered phantom prepared by depositing a thin layer of paraffin over acetaminophen. A good correlation was observed between the OCT images and the Raman signal. The system was also used to record OCT and Raman images of a resected mucosal tissue sample. While OCT image showed the presence of epithelial and stromal layers, Raman spectra measured from these layers confirmed the biochemical difference between the two. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Background: We report the results of a clinical in vivo study to evaluate the potential of fluorescence spectroscopy for differential diagnosis of oral mucosal malignant and potentially malignant lesions. Materials and Methods: The study involved 26 healthy volunteers and 144 patients enrolled for routine medical examination of the oral cavity at the outpatient department of the Tata Memorial Hospital, Mumbai. In vivo autofluorescence spectra were recorded using a N2 laser based portable fluorimeter developed in-house. The different tissue sites investigated belonged to either of the four histopathologic categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A multivariate statistical algorithm capable of direct multi-class classification was used to predict pathological designations. Results: With respect to histopathology as the "gold standard", the diagnostic algorithm was found to provide an accuracy of 82, 76, 81 and 85% based on leave-one-patient-out cross-validation in classifying the oral tissue spectra into four different pathology classes - SCC, OSMF, LP, and normal squamous tissue - respectively. When the algorithm was employed for delineating the normal oral tissues from all the abnormal oral tissues including SCC, OSMF and LP put together, a sensitivity of 98% and a specificity of 100% were obtained. Conclusion: The results suggest that it is possible to objectively classify the oral tissue into different pathology classes based on their in vivo autofluorescence spectra. Thus, the technique can potentially improve oral screening efforts in low resource settings where clinical expertise and resources are limited. [ABSTRACT FROM AUTHOR]