Fluorescence photo-bleaching of urine and its applicability in oral cancer diagnosis.

Photo-stability of urine is of crucial importance for the applicability of fluorescence spectroscopy of urine samples for diagnosis of cancer. We report the results of a detailed study on fluorescence photo-bleaching of human urine samples. We also present the results of a preliminary investigation on evaluation of the applicability of photo-bleaching characteristics of urine for discriminating patients with oral cancer from healthy volunteers. The time-lapse fluorescence induced by continuous shining of 405 nm radiation from a diode laser was recorded from the urine samples obtained from 18 patients with oral cancer as well as from 22 healthy volunteers with history of no known major illness in the past two months. The integrated fluorescence intensity (ΣI), calculated for each spectrum, was found to decrease with time till a point after which no further decrease was observed. Further, while significant differences were observed in the spectra of cancerous patients and healthy volunteers, these differences were found to be varying with time till the intensities of the observed fluorescence spectra corresponding to the two categories of urine samples became stable. The curve, generated by plotting ΣI vs. time, was found to be best fitted (R2 > 0.95) with a double-exponential decay function. The photo-bleaching constants, obtained from curve-fitting, were found to have statistically significant differences corresponding to the urine samples of cancerous patients and healthy volunteers. A classification algorithm developed based on nearest-mean classifier (NMC) and applied on the photo-bleaching constants in leave-one-subject-out cross-validation mode was found to provide a sensitivity and specificity of up to ∼ 86% in discriminating the two categories of urine samples.

Inverse spatially-offset Raman spectroscopy using optical fibers: An axicon lens-free approach.

Inverse spatially offset Raman spectroscopy (I-SORS) seeks to interrogate deep inside a Raman-active, layered, diffusely scattering sample. It makes a collimated laser beam incident onto the sample surface in the form of concentric illumination rings (of varying radii) from whose center the back-scattered Raman signal is collected for detection. Since formation of illumination rings of different sizes requires an axicon to be moved along the axis of the collimated laser beam and axicons below a certain minimum size (~1 inch) are not readily available, this classical configuration incorporating an axicon cannot be used for designing a compact I-SORS probe of narrower diameter. We report a novel scheme of implementing I-SORS which overcomes this limitation by implementing ring illumination and point collection using two multi-mode optical fibers. An important advantage of the proposed scheme is that unlike the previously reported inverse SORS configurations, it does not require physical movement of any of the optical components for generating spatial offsets needed for probing sub-surface depths. Another advantage is its fiber-optic configuration which is ideally suited for designing a compact and pencil-sized I-SORS probe, often desired in many practical situations for carrying out depth-sensitive Raman measurements in situ from a layered turbid sample.

Nanotrap-Enhanced Raman Spectroscopy: An Efficient Technique for Trace Detection of Bioanalytes.

Reliable diagnosis of disease using body fluids requires sensitive and accurate detection of disease-specific analytes present in the fluid. In recent years, there has been increasing interest in using surface-enhanced Raman spectroscopy (SERS) for this purpose. The demonstrable signal enhancement and sensitivity of SERS makes it ideally suited for detection of a trace quantity of any analyte. However, lack of reproducibility along with large spatial variability in the measured Raman intensities due to differential (and often random) distribution of surface "hot spots" limits its routine clinical use. We propose here a technique, nanotrap-enhanced Raman spectroscopy (NTERS), for overcoming these long-standing limitations and challenges of SERS. In this technique, hot spots are formed by drying up a microvolume drop of the liquid, containing the mixture of nanoparticles and analytes in the focal volume of the Raman excitation laser, and the Raman signal is detected from these spots containing the analytes localized within the nanoparticle aggregates. The performance of the technique was evaluated in detecting trace quantities of two Raman-active analytes, Rhodamine 6G (R6G) and urea. It was found that R6G and urea could be detected down to a concentration of 50 nM with signal-to-noise ratio (SNR) value of ∼75 and 4 mM with SNR value of ∼500, respectively. A comparison with SERS revealed that NTERS not only had significantly superior (around 2 orders of magnitude) signal enhancement but also had high reproducibility because of its intrinsic ability to form nanoparticle aggregates with high repetitiveness. Another advantage of NTERS is its simplicity and cost effectiveness as it does not require any specialized substrate.

Simultaneous photoreduction and Raman spectroscopy of red blood cells to investigate the effects of organophosphate exposure.

Simultaneous photoreduction and Raman spectroscopy with 532 nm laser has been used to study the effects of organophosphate (chlorpyrifos [CPF]) exposure on human red blood cells (RBCs). Since in RBCs, auto-oxidation causes oxidative stress, which, in turn, is balanced by the cellular detoxicants, any possible negative effect of CPF on this balance should results in an increased level of damaged (permanently oxygenated) hemoglobin. Therefore, when 532 nm laser, at a suitable power, was applied to photoreduce the cells, only common oxygenated form of hemoglobin got photoreduced leaving the permanently oxygenated hemoglobin detectable in the Raman spectra simultaneously excited by the same laser. Using the technique effects of CPF to build up oxidative stress on RBCs could be detected at concentrations as low as 10 ppb from a comparison of relative strengths of different Raman bands. Experiments performed using simultaneously exposing the cells, along with CPF, to H2 O2 (oxidative agent) and/or 3-Aminotriazole (inhibitor of anti-oxidant catalase), suggested role of CPF to suppress the cellular anti-oxidant mechanism. Since the high level of damaged hemoglobin produced by the action of CPF (at concentrations >100 ppm) is expected to cause membrane damage, atomic force microscopy (AFM) was used to identify such damages.Upper panel: Raman spectra of normal, photoreduced CPF exposed and unexposed RBCs. Lower panel: The weak Fe-O2 Raman band for CPF exposed cells shown on the left. The AFM images of unexposed and exposed cells are shown on the right. Scale bar, 2.5 µm.

Red blood cell membrane damage by light-induced thermal gradient under optical trap.

Rapid membrane damage of optically trapped red blood cells (RBCs) was observed at trapping powers ≥280 mW. An excellent agreement between the estimated laser-induced thermal gradient across trapped cell's membrane and that typically required for membrane electropermeabilization suggests a mechanism involving temperature gradient-induced electropermeabilization of membrane. Also the rapid collapse of the trapped cell due to membrane rupture was seen to cause shock waves in the surroundings permeabilizing nearby untrapped cells. When the experiments were carried out with RBCs collected from type II diabetic patients, a noticeable change in the damage rate compared to normal RBCs was seen suggesting a novel optical diagnosis method for the disease.

Changes in hemoglobin-oxygen affinity with shape variations of red blood cells.

Shape variations of red blood cells (RBCs) are known to occur upon exposure to various drugs or under diseased conditions. The commonly observed discocytic RBCs can be transformed to echinocytic or stomatocytic shape under such conditions. Raman spectra of the three major shape variations, namely discocyte, echinocyte, and stomatocyte, of RBCs were studied while subjecting the cells to oxygenated and deoxygenated conditions. Analysis of the recorded spectra suggests an increased level of hemoglobin (Hb)-oxygen affinity for the echinocytes. Also, some level of Hb degradation could be noticed for the deoxygenated echinocytes. The effects may arise from a reduced level of intracellular adenosine triphosphate in echinocytic cells and an increased fraction of submembrane Hb.

Tunable Spin dependent beam shift by simultaneously tailoring geometric and dynamical phases of light in inhomogeneous anisotropic medium.

Spin orbit interaction and the resulting Spin Hall effect of light are under recent intensive investigations because of their fundamental nature and potential applications. Here, we report an interesting manifestation of spin Hall effect of light and demonstrate its tunability in an inhomogeneous anisotropic medium exhibiting spatially varying retardance level. In our system, the beam shift occurs only for one circular polarization mode keeping the other orthogonal mode unaffected, which is shown to arise due to the combined spatial gradients of the geometric phase and the dynamical phase of light. The constituent two orthogonal circular polarization modes of an input linearly polarized light evolve in different trajectories, eventually manifesting as a large and tunable spin separation. The spin dependent beam shift and the demonstrated principle of simultaneously tailoring space-varying geometric and dynamical phase of light for achieving its tunability (of both magnitude and direction), may provide an attractive route towards development of spin-optical devices.

Inverse SORS for detecting a low Raman-active turbid sample placed inside a highly Raman-active diffusely scattering matrix - A feasibility study.

The broad range of applications of spatially-offset Raman spectroscopy (SORS) were found to involve samples having only marginal differences in Raman cross-sections between the surface and subsurface targets. We report the results of a feasibility study to evaluate the potential of the approach to identify the presence of a very low Raman-active turbid sample placed inside a highly Raman-active diffusely scattering matrix. Paraffin sandwiched tissue blocks prepared by embedding slices of chicken muscle tissue into solid paraffin blocks were employed as representative samples for the study. It was found that in contrast to the several millimetres of probing depth reported in the earlier applications, the Raman signatures of tissue were best recovered when it was located beneath the surface of the paraffin block at a depth of around a millimetre, beyond which the quality of recovery was increasingly poorer. However, the probing depth could be further increased by increasing the thickness of the embedded tissue sections. The results clearly suggest that though the probing depth achievable under the current condition is less than that found in previous applications, nevertheless it is sufficient for various other applications that would not require probing as deep as was required earlier.

Depth-sensitive Raman spectroscopy combined with optical coherence tomography for layered tissue analysis.

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.

Effect of normal variations on disease classification of Raman spectra from cervical tissue.

In this paper, we examine how variations in normal tissue can influence disease classification of Raman spectra. Raman spectra from normal areas may be affected by previous disease or proximity to areas of dysplasia. Spectra were acquired in vivo from 172 patients and classified into five tissue categories: true normal (no history of disease), previous disease normal (history of disease, current normal diagnosis), adjacent normal (disease on cervix, spectra acquired from visually normal area), low grade, and high grade. Taking into account the various "normal" states of the tissue before statistical analysis led to a disease classification accuracy of 97%. These results indicate that abnormal changes significantly affect Raman spectra, even when areas are histopathologically normal. The sensitivity of Raman spectroscopy to subtle biochemical differences must be considered in order to successfully implement it in a clinical setting for diagnosing cervical dysplasia and cancer.

Autofluorescence and diffuse reflectance spectroscopy and spectral imaging for breast surgical margin analysis.

BACKGROUND AND OBJECTIVE: Most women with early stage breast cancer have the option of breast conserving therapy, which involves a partial mastectomy for removal of the primary tumor, usually followed by radiotherapy. The presence of tumor at or near the margin is strongly correlated with the risk of local tumor recurrence, so there is a need for a non-invasive, real-time tool to evaluate margin status. This study examined the use of autofluorescence and diffuse reflectance spectroscopy and spectral imaging to evaluate margin status intraoperatively. MATERIALS AND METHODS: Spectral measurements were taken from the surface of the tissue mass immediately following removal during partial mastectomies and/or from tissues immediately after sectioning by surgical pathology. A total of 145 normal spectra were obtained from 28 patients, and 34 tumor spectra were obtained from 12 patients. RESULTS: After correlation with histopathology, a multivariate statistical algorithm classified the spectra as normal (negative margins) or tumor (positive margins) with 85% sensitivity and 96% specificity. A separate algorithm achieved 100% classification between neo-adjuvant chemotherapy-treated tissues and non-treated tissues. Fluorescence and reflectance-based spectral images were able to demarcate a calcified lesion on the surface of a resected specimen as well. CONCLUSION: Fluorescence and reflectance spectroscopy could be a valuable tool for examining the superficial margin status of excised breast tumor specimens, particularly in the form of spectral imaging to examine entire margins in a single acquisition.

Fluorescence spectroscopy for noninvasive early diagnosis of oral mucosal malignant and potentially malignant lesions.

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.

Spatially offset Raman spectroscopy of layered soft tissues.

Raman spectroscopy has been widely used for cancer diagnosis, but conventional forms provide limited depth information. Spatially offset Raman spectroscopy (SORS) can solve the depth issue, but it has only been used to detect hard tissues such as bone. We explore the feasibility of using SORS to discriminate two layers of soft tissue. Measurements were taken with individual source and detector fibers at a number of spatial offsets from samples consisting of various thicknesses of normal human breast tissues overlying breast tumors. Results show that SORS can detect tumors beneath normal tissue, marking, to the best of our knowledge, the first application of SORS for discriminating two layers of soft tissue.

Comparison of autofluorescence, diffuse reflectance, and Raman spectroscopy for breast tissue discrimination.

For a given diagnostic problem, important considerations are the relative performances of the various optical biopsy techniques. A comparative evaluation of fluorescence, diffuse reflectance, combined fluorescence and diffuse reflectance, and Raman spectroscopy in discriminating different histopathologic categories of human breast tissues is reported. Optical spectra were acquired ex vivo from a total of 74 breast tissue samples belonging to 4 distinct histopathologic categories: invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), fibroadenoma (FA), and normal breast tissue. A probability-based multivariate statistical algorithm capable of direct multiclass classification was developed to analyze the diagnostic content of the spectra measured from the same set of breast tissue sites with these different techniques. The algorithm uses the theory of nonlinear maximum representation and discrimination feature for feature extraction, and the theory of sparse multinomial logistic regression for classification. The results reveal that the performance of Raman spectroscopy is superior to that of all others in classifying the breast tissues into respective histopathologic categories. The best classification accuracy was observed to be approximately 99%, 94%, 98%, and 100% for IDC, DCIS, FA, and normal breast tissues, respectively, on the basis of leave-one-sample-out cross-validation, with an overall accuracy of approximately 99%.

In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy.

BACKGROUND AND OBJECTIVES: Nonmelanoma skin cancers, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most common skin cancers, presenting nearly as many cases as all other cancers combined. The current gold-standard for clinical diagnosis of these lesions is histopathologic examination, an invasive, time-consuming procedure. There is thus considerable interest in developing a real-time, automated, noninvasive tool for nonmelanoma skin cancer diagnosis. In this study, we explored the capability of Raman microspectroscopy to provide differential diagnosis of BCC, SCC, inflamed scar tissue, and normal tissue in vivo. STUDY DESIGN: Based on the results of previous in vitro studies, we developed a portable confocal Raman system with a handheld probe for clinical study. Using this portable system, we measured Raman spectra of 21 suspected nonmelanoma skin cancers in 19 patients with matched normal skin spectra. These spectra were input into nonlinear diagnostic algorithms to predict pathological designation. RESULTS: All of the BCC (9/9), SCC (4/4), and inflamed scar tissues (8/8) were correctly predicted by the diagnostic algorithm, and 19 out of 21 normal tissues were correctly classified. This translates into a 100% (21/21) sensitivity and 91% (19/21) specificity for abnormality, with a 95% (40/42) overall classification accuracy. CONCLUSIONS: These findings reveal Raman microspectroscopy to be a viable tool for real-time diagnosis and guidance of nonmelanoma skin cancer resection.

Raman microspectroscopy for skin cancer detection in vitro.

We investigate the potential of near-infrared Raman microspectroscopy to differentiate between normal and malignant skin lesions. Thirty-nine skin tissue samples consisting of normal, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma from 39 patients were investigated. Raman spectra were recorded at the surface and at 20-microm intervals below the surface for each sample, down to a depth of at least 100 microm. Data reduction algorithms based on the nonlinear maximum representation and discrimination feature (MRDF) and discriminant algorithms using sparse multinomial logistic regression (SMLR) were developed for classification of the Raman spectra relative to histopathology. The tissue Raman spectra were classified into pathological states with a maximal overall sensitivity and specificity for disease of 100%. These results indicate the potential of using Raman microspectroscopy for skin cancer detection and provide a clear rationale for future clinical studies.

Detecting temporal and spatial effects of epithelial cancers with Raman spectroscopy.

Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.

A probability-based spectroscopic diagnostic algorithm for simultaneous discrimination of brain tumor and tumor margins from normal brain tissue.

This paper reports the development of a probability-based spectroscopic diagnostic algorithm capable of simultaneously discriminating tumor core and tumor margins from normal human brain tissues. The algorithm uses a nonlinear method for feature extraction based on maximum representation and discrimination feature (MRDF) and a Bayesian method for classification based on sparse multinomial logistic regression (SMLR). Both the autofluorescence and the diffuse-reflectance spectra acquired in vivo from patients undergoing craniotomy or temporal lobectomy at the Vanderbilt University Medical Center were used to train and validate the algorithm. The classification accuracy was observed to be approximately 96%, 80%, and 97% for the tumor, tumor margin, and normal brain tissues, respectively, for the training data set and approximately 96%, 94%, and 100%, respectively, for the corresponding tissue types in an independent validation data set. The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need for a hierarchical multi-step binary classification scheme. Further, the probabilistic nature of the algorithm makes it possible to quantitatively assess the certainty of the classification and recheck the samples that are classified with higher relative uncertainty.

Comparison of spectral variation from spectroscopy to spectral imaging.

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.

Relevance vector machine for optical diagnosis of cancer.

BACKGROUND AND OBJECTIVES: A probability-based, robust diagnostic algorithm is an essential requirement for successful clinical use of optical spectroscopy for cancer diagnosis. This study reports the use of the theory of relevance vector machine (RVM), a recent Bayesian machine-learning framework of statistical pattern recognition, for development of a fully probabilistic algorithm for autofluorescence diagnosis of early stage cancer of human oral cavity. It also presents a comparative evaluation of the diagnostic efficacy of the RVM algorithm with that based on support vector machine (SVM) that has recently received considerable attention for this purpose. STUDY DESIGN/MATERIALS AND METHODS: The diagnostic algorithms were developed using in vivo autofluorescence spectral data acquired from human oral cavity with a N(2) laser-based portable fluorimeter. The spectral data of both patients as well as normal volunteers, enrolled at Out Patient department of the Govt. Cancer Hospital, Indore for screening of oral cavity, were used for this purpose. The patients selected had no prior confirmed malignancy and were diagnosed of squamous cell carcinoma (SCC), Grade-I on the basis of histopathology of biopsy taken from abnormal site subsequent to acquisition of spectra. Autofluorescence spectra were recorded from a total of 171 tissue sites from 16 patients and 154 healthy squamous tissue sites from 13 normal volunteers. Of 171 tissues sites from patients, 83 were SCC and the rest were contralateral uninvolved squamous tissue. Each site was treated separately and classified via the diagnostic algorithm developed. Instead of the spectral data from uninvolved sites of patients, the data from normal volunteers were used as the normal database for the development of diagnostic algorithms. RESULTS: The diagnostic algorithms based on RVM were found to provide classification performance comparable to the state-of-the-art SVMs, while at the same time explicitly predicting the probability of class membership. The sensitivity and specificity towards cancer were up to 88% and 95% for the training set data based on leave- one-out cross validation and up to 91% and 96% for the validation set data. When implemented on the spectral data of the uninvolved oral cavity sites from the patients, it yielded a specificity of up to 91%. CONCLUSIONS: The Bayesian framework of RVM formulation makes it possible to predict the posterior probability of class membership in discriminating early SCC from the normal squamous tissue sites of the oral cavity in contrast to dichotomous classification provided by the non-Bayesian SVM. Such classification is very helpful in handling asymmetric misclassification costs like assigning different weights for having a false negative result for identifying cancer compared to false positive. The results further demonstrate that for comparable diagnostic performances, the RVM-based algorithms use significantly fewer kernel functions and do not need to estimate any hoc parameters associated with the learning or the optimization technique to be used. This implies a considerable saving in memory and computation in a practical implementation.