Raman spectroscopy is a powerful diagnostic tool, enabling tissue identification and classification. Mostly, the so-called fingerprint (approximately 400-1800 cm(-1)) spectral region is used. In vivo application often requires small flexible fiber-optic probes, and is hindered by the intense Raman signal that is generated in the fused silica core of the fiber. This necessitates filtering of laser light, which is guided to the tissue, and of the scattered light collected from the tissue, leading to complex and expensive designs. Fused silica has no Raman signal in the high wave number region (2400-3800 cm(-1)). This enables the use of a single unfiltered fiber to guide laser light to the tissue and to collect scattered light in this spectral region. We show, by means of a comparison of in vitro Raman microspectroscopic maps of thin tissue sections (brain tumors, bladder), measured both in the high wave number region and in the fingerprint region, that essentially the same diagnostic information is obtained in the two wave number regions. This suggests that for many clinical applications the technological hurdle of designing and constructing suitable fiber-optic probes may be eliminated by using the high wave number region and a simple piece of standard optical fiber.

The detection of dysplasia and early cancer is important because of the improved survival rates associated with early treatment of cancer. Raman spectroscopy is sensitive to the changes in molecular composition and molecular conformation that occur in tissue during carcinogenesis, and recent developments in fiber-optic probe technology enable its application as an in vivo technique. In this study, the potential of Raman spectroscopy for in vivo classification of normal and dysplastic tissue was investigated. A rat model was used for this purpose, in which dysplasia in the epithelium of the palate was induced by topical application of the carcinogen 4-nitroquinoline 1-oxide. High quality in vivo spectra of normal and dysplastic rat palate tissue, obtained using signal integration times of 100 s were used to create tissue classification models based on multivariate statistical analysis methods. These were tested with an independent set of in vivo spectra, obtained using signal collection times of 10 s. The best performing model, in which signal variance due to signal contributions of the palatal bone was eliminated, was able to distinguish between normal tissue, low-grade dysplasia, and high-grade dysplasia/carcinoma in situ with a selectivity of 0.93 and a sensitivity of 0.78 for detecting low-grade dysplasia and a specificity of 1 and a sensitivity of 1 for detecting high-grade dysplasia/ carcinoma in situ.

Tumor-positive resection margins are a major problem in oral cancer surgery. High-wavenumber Raman spectroscopy is a reliable technique to determine the water content of tissues, which may contribute to differentiate between tumor and healthy tissue. The aim of this study was to examine the use of Raman spectroscopy to differentiate tumor from surrounding healthy tissue in oral squamous cell carcinoma. From 14 patients undergoing tongue resection for squamous cell carcinoma, the water content was determined at 170 locations on freshly excised tongue specimens using the Raman bands of the OH-stretching vibrations (3350-3550 cm(-1)) and of the CH-stretching vibrations (2910-2965 cm(-1)). The results were correlated with histopathological assessment of hematoxylin and eosin stained thin tissue sections obtained from the Raman measurement locations. The water content values from squamous cell carcinoma measurements were significantly higher than from surrounding healthy tissue (p-value < 0.0001). Tumor tissue could be detected with a sensitivity of 99% and a specificity of 92% using a cutoff water content value of 69%. Because the Raman measurements are fast and can be carried out on freshly excised tissue without any tissue preparation, this finding signifies an important step toward the development of an intraoperative tool for tumor resection guidance with the aim of enabling oncological radical surgery and improvement of patient outcome.

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This article describes a unique noninvasive capability to determine the concentration (in mg/cm3) and total amount of topically applied materials in the skin (in μg/cm2 of skin surface). It is based on in vivo confocal Raman spectroscopy. A theoretical derivation is given of a general method to calculate a concentration ratio from a Raman spectrum of a material in a medium, which can be a solvent or other matrix, such as the skin. A practical implementation of the method is then presented along with a clarification of the assumptions used and applied to a quantitative analysis of the in vivo skin penetration of trans‐retinol and propylene glycol (PG). A comparison was made between the concentrations profiles of retinol and PG found in the skin and the concentrations of retinol and PG that had been applied to the skin. Determination of the amount of these materials in the skin at different timepoints after topical application also enabled a straightforward calculation of the flux of materials into the skin (in μg cm−2 h).

Abstract-Quantitative characterization of atherosclerotic plaque composition with standard histopathological methods remains limited to sectioned plaques. Raman spectroscopy enables nondestructive quantification of atherosclerotic plaque composition. We used Raman spectroscopy to study the effects of diet and lipid-lowering therapy on plaque development in apolipoprotein (APO) E*3-Leiden transgenic mice. Raman spectra were obtained over the full width and entire length of the ascending aorta and aortic arch. Spectra were modeled to calculate the relative dry weights of cholesterol and calcium salts, and quantitative maps of their distribution were created. In male mice (nϭ20) that received a high-fat/high-cholesterol (HFC) diet for 0, 2, 4, or 6 months, Raman spectroscopy showed good correlation between cholesterol accumulation and total serum cholesterol exposure (rϷ0.87, PϽ0.001). In female mice (nϭ10) that were assigned to an HFC diet, with or without 0.01% atorvastatin, a strong reduction in cholesterol accumulation (57%) and calcium salts (97%) (PϽ0.01) was demonstrated in the atorvastatin-treated group. In conclusion, Raman spectroscopy can be used to quantitatively study the size and distribution of depositions of cholesterol and calcification in APOE*3-Leiden transgenic mice. This study encourages Raman spectroscopy for the quantitative investigation of atherosclerosis and lipid-lowering therapy in larger animals or humans in vivo. Key Words: atherosclerosis Ⅲ Raman spectroscopy Ⅲ cholesterol Ⅲ calcification Ⅲ lipid-lowering treatment P rogression of atherosclerosis is dependent on the amount of lipids that accumulate in the intima of arteries. 1,2 Several drugs have been developed that successfully lower the plasma cholesterol concentration, thereby reducing the rate of progression of atherosclerosis. 3,4 However, despite the success of these drugs, a considerable number of treated patients will still encounter an ischemic event. To improve management of patients with atherosclerosis, a more thorough understanding of plaque progression and regression in vivo, at the chemical level, is required.Raman spectroscopy is a technique that can provide this kind of information. It provides quantitative information about the molecular composition of a sample and enables the nondestructive examination of small volumes of tissue. 5 A Raman method to quantify the relative amounts of protein, cholesterol, adventitial fat, and calcium salts (CS) in the human coronary artery wall has been developed by Brennan et al. 6 Recently, it was demonstrated that high signal-to-noise Raman spectra can be obtained from the aortic arch and arteries of sheep in vivo, in the presence of blood flow, by using specially designed Raman fiber-optic catheters and a transluminal approach. 7,8 We studied atherosclerotic plaque formation in transgenic mice that were fed a high cholesterol-containing diet. These mice, carrying the dysfunctional apoE variant from patients with a dominantly inherited form of familial dysbetalipoproteinemia (APOE...

The application of in vivo Raman spectroscopy for clinical diagnosis demands dedicated software that can perform the necessary signal processing and subsequent (multivariate) data analysis, enabling clinically relevant parameters to be extracted and made available in real time. Here we describe the design and implementation of a software package that allows for real-time signal processing and data analysis of Raman spectra. The design is based on automatic data exchange between Grams, a spectroscopic data acquisition and analysis program, and Matlab, a program designed for array-based calculations. The data analysis software has a modular design providing great flexibility in developing custom data analysis routines for different applications. The implementation is illustrated by a computationally demanding application for the classification of skin spectra using principal component analysis and linear discriminant analysis.

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