Quantitative Characterization of Biological Tissue Using Optical Spectroscopy

Transcription

1 Irene Georgakoudi Massachusetts Institute of Technology Cambridge, Massachusetts Jason T. Motz Massachusetts Institute of Technology Cambridge, Massachusetts Vadim Backman Northwestern University Evanston, Illinois George Angheloiu The Cleveland Clinic Foundation Cleveland, Ohio Abigail S. Haka Massachusetts Institute of Technology Cambridge, Massachusetts Markus Müller Massachusetts Institute of Technology Cambridge, Massachusetts Ramachandra Dasari Massachusetts Institute of Technology Cambridge, Massachusetts Michael S. Feld Massachusetts Institute of Technology Cambridge, Massachusetts 31 Quantitative Characterization of Biological Tissue Using Optical Spectroscopy 31.1 Introduction 31.2 Characterization of Tissue Biochemistry Using Fluorescence Spectroscopy in Vivo 31.3 Characterization of Bulk Tissue Optical Properties Using Diffuse Reflectance Spectroscopy in Vivo 31.4 Characterization of Cellular and Subcellular Morphology Using Light-Scattering Spectroscopy 31.5 Characterization of Tissue Molecular Composition Using Near-Infrared Raman Spectroscopy 31.6 Enhanced Tissue Characterization via Combined Use of Spectroscopic Techniques Tri-Modal Spectroscopy for Characterization and Detection of Precancerous Lesions in Vivo Bi-Modal Spectroscopy for Characterizing Atherosclerotic ex Vivo Lesions Fluorescence and Raman Spectroscopy for Characterizing Microscopic Ceroid Deposits in Coronary Artery Samples 31.7 Conclusion Acknowledgments References 31.1 Introduction Spectroscopic techniques examine different types of light tissue interactions and provide biochemical and morphological information at the molecular, cell, and tissue levels in a noninvasive way. Because light delivery and collection are compatible with optical fibers and data analysis can be achieved in real time, spectroscopic information can serve as a powerful tool for assessing the state of tissue in vivo thus, guiding surgery or biopsy or monitoring the effects of treatment. Ultimately, optical techniques could eliminate the need for biopsy, at least in some cases, and allow for a single triage visit during which detection and treatment of a lesion could be combined. However, to optimize the use of optical methods in disease detection and treatment, it is important to understand and quantify the information provided by the detected optical signals. Spectroscopic techniques are ideal for this purpose. Three different methods have been used to establish the presence of distinct spectral features for normal and diseased tissues in vivo. Empirical techniques use intensity information at specific wavelengths or

2 wavelengh ranges. Such techniques are easy to implement, but they do not use the characteristics of the full spectrum and lack quantitation. Statistical techniques, such as principal component analysis, are based on the analysis of the entire spectrum. However, this approach does not provide insights into the origins of the detected changes. In addition, statistical analysis methods usually assume a linear relationship between the components contributing to a given spectrum. Thus, extraction of quantitative information about the source of the spectroscopic signals is often difficult because of the turbid tissue nature or interference between different types of spectroscopic features (for example, fluorescence and Raman, or fluorescence, scattering, and absorption). Thus, it is necessary to develop models that take into account the optical, morphological, and biochemical properties of tissue in a quantitative way. Parameters extracted from model-based techniques can be used to classify tissue (e.g., normal vs. diseased) or to quantify tissue components (e.g., blood analyte concentrations). In this chapter, we review how four spectroscopic techniques (fluorescence, diffuse reflectance, light scattering and Raman spectroscopy) can be used to extract quantitative information about tissue biochemistry, organization, morphology, and molecular composition and the corresponding changes that take place during the development of disease. In addition, we discuss the complementary character of these techniques and provide examples demonstrating the synergy and enhancement that can be achieved by combining spectroscopic information Characterization of Tissue Biochemistry Using Fluorescence Spectroscopy in Vivo Fluorescence is one of the most widely used spectroscopic techniques at the clinical and the basic science levels. 1 It has been used as a tool for the detection of endogenous and exogenous chromophores as a means of localizing lesions and tailoring dosimetry for treatments such as photodynamic therapy. 2 In this section, we will focus on endogenous tissue fluorophores. Promising results have been reported on the use of steady-state and time-resolved fluorescence spectroscopy as a diagnostic modality in a number of tissues, including the gastrointestinal tract, 3 5 the uterine cervix, 6 8 the skin, 6 9 the bladder, 10 the oral cavity, and the lung For most epithelial tissues, a small number of endogenous fluorophores are present that can be excited in the 300- to 600-nm range. Among those, tryptophan, collagen, elastin, NAD(P)H, FAD, and porphyrins are the most prominent. Only two or three of these fluorophores are excited simultaneously for a specific excitation wavelength. Despite this fact, extraction of quantitative information about the fluorescence contributions of a specific chromophore is not trivial. Most of the difficulties are associated with the fact that measured tissue fluorescence can be highly distorted by tissue scattering and absorption. In most cases, statistical or empirical algorithms are used to assess the sensitivity and specificity with which lesions can be distinguished from normal tissues based on their fluorescence characteristics. Empirical algorithms typically use the value or the ratio of the fluorescence intensity at specific excitation emission wavelengths or wavelength ranges. 3 This is the approach adopted by most fluorescence imaging diagnostic systems used clinically. 13,14 Principal component analysis is also a very useful statistical tool that is often used to decompose the spectra within a given data set into a linear combination of orthogonal basis spectra called principal components (PCs). 15 The first PC accounts for the spectral features that vary the most, and subsequent PCs represent features with progressively smaller variance. To describe a specific spectrum, a linear combination of PCs is used, with each PC weighed by the appropriate PC score. Typically, the values of these PC scores are used to determine the spectral features that are different between normal and diseased tissues and to develop diagnostic algorithms. For example, the scores of selected PCs describing measured fluorescence spectra at 337-, 380-, and 460-nm excitation were used to separate cervical squamous intraepithelial lesions (SILs) from non-sils with a sensitivity of 82% and a specificity of 68%. 6 Statistical algorithms based on neural network nonlinear methods have also been developed and have shown, in some cases, superior diagnostic performance over multivariate statistical analysis-based algorithms. 16

3 Such techniques are often useful in identifying spectral regions within which diagnostically useful fluorescence changes exist, and they can provide qualitative insights into the origins of these changes. However, in some cases subtle fluorescence differences can be masked by interferences introduced by tissue scattering and absorption. Thus, to acquire reliable quantitative information about the biochemical changes that take place during the development of disease and are represented in the tissue fluorescence, it is necessary to remove these distortions. Empirical and more theoretically rigorous models have been developed to achieve this. An empirical model developed by Richards-Kortum et al. 17 expressed the measured fluorescence as the product of two factors: (1) a linear combination of all the fluorophore contributions representing intrinsic fluorescence and (2) an attenuation factor representing broadband attenuation due to scattering, and oxyhemoglobin attenuation due to blood absorption. This model has been used to describe fluorescence excited at 476 nm from normal and diseased human arterial ex vivo tissues in terms of the intrinsic fluorescence from structural proteins (collagen and elastin) and ceroid, and the attenuation due to hemoglobin and structural proteins. 17 Using the extracted fluorescence contributions for ceroid and structural proteins, diagnostic algorithms were developed that separated normal from diseased tissue with 91% sensitivity and 85% specificity. This model has also been employed to describe measured fluorescence spectra excited at 337 nm and acquired in vivo from colposcopically normal and abnormal cervical tissues. 18 In this case, the measured fluorescence was described in terms of the intrinsic fluorescence from collagen, elastin, NAD(P)H, and FAD, and attenuation due to oxyhemoglobin and scattering. The latter was represented by a constant. After normalizing the collagen contribution for each site of each patient to the average collagen contribution to the fluorescence of the normal sites of the same patient, a decrease was observed in the collagen fluorescence of precancerous tissues compared to colposcopically normal ones. A trend toward elevated levels of the normalized relative NAD(P)H contributions was also reported for precancerous tissues when compared to normal tissues in the same patient. To observe this trend, spectra were normalized to their peak intensity prior to model analysis. The relative NAD(P)H fluorescence contribution of each site was then divided by the average NAD(P)H contribution to the normal sites in the same patient. This type of analysis provided useful information for understanding the origins of some of the observed measured fluorescence changes. However, because of the required normalizations, it was not very useful diagnostically in a clinical setting. It has been recognized for some time that the diffuse reflectance spectrum, measured simultaneously with fluorescence, can also provide a means to remove absorption and scattering distortions in the measured tissue fluorescence spectrum, and thus to extract the intrinsic fluorescence. This was implemented in early fluorescence studies designed to monitor NAD(P)H in metabolically active tissues, such as the brain, heart and liver. The basic rationale behind this approach is that fluorescence and reflectance photons are distorted similarly by scattering and absorption. A comprehensive review of relevant work has been presented by Ince et al. 19 Initial expressions involved linear or nonlinear combinations of the fluorescence and reflectance spectral features at specific wavelengths. Unfortunately, these models are applicable only for the particular wavelength regimes for which they were developed and tested, and are not sufficient to recover the entire intrinsic tissue fluorescence spectrum line shape and intensity. To achieve that, it is necessary to take into account the wavelength-dependent optical properties of tissue in a more rigorous manner. To this effect, an analytic model based on a photon migration picture developed from Monte Carlo simulations was introduced by Wu et al. 28 This model combines fluorescence and reflectance measurements acquired over the same wavelength range using identical light delivery and collection geometries. An empirical modification of this model that takes into account changes in the optical properties at the excitation wavelength has been reported recently by Finlay et al. 29 This expression was used to follow quantitatively the kinetics of protoporphyrin IX photobleaching and photoproduct formation in the normal rat skin in vivo. Gardner et al. 30 used Monte Carlo simulations to relate the propagation of laser excitation light and fluorescence to the diffuse reflectance. With these empirically obtained expressions, an expression for the intrinsic fluorescence was derived. Durkin et al. 31 used Kubelka-Munk absorption and scattering coefficients

4 obtained from diffuse reflectance and transmittance experiments to predict the intrinsic fluorescence spectrum. Although such a model could be very useful for ex vivo studies, the acquisition of parameters from transmittance measurements renders this approach impractical for in vivo tissue measurements. Zhadin et al. 32 expressed reflectance and fluorescence in terms of the medium s darkness, a variable defined as the ratio of the absorption coefficient to the reduced scattering coefficient. The darkness parameter was extracted by inverting the reflectance and was used to extract the intrinsic fluorescence line shape from the measured fluorescence. Most of these studies have been limited to wavelength ranges within which hemoglobin and water absorption are not very strong; 28 30,32,33 however, a large number of fluorescence studies have been conducted in the 400-nm emission range, 1,2 where important tissue chromophores, such as collagen and NAD(P)H, fluoresce strongly. Unfortunately, the Soret band of hemoglobin absorption is also in this spectral region, with a peak in tissue absorption at approximately 420 nm. This absorption can give rise to a dip in the measured bulk fluorescence that can be misinterpreted because the spectra are no longer simply a linear sum of spectral contributions from endogenous fluorophores such as collagen, NAD(P)H, elastin, etc. Such interference can not only lead to misinterpretation of the biochemical information conveyed by the measured fluorescence, but they can also mask small biochemical changes that take place in diseased tissue. Therefore, it is important to extract the tissue intrinsic fluorescence over a wide emission range that includes this important diagnostic region. Recently, the photon-migration-based model developed by Wu et al. 28 was modified to extend its validity in regimes of significant absorption. 34 The ability of this model to recover the intrinsic fluorescence line shape and intensity has been validated using tissue phantoms with a wide range of physiologically relevant scattering and absorption properties. 35 This model has been used clinically to recover the intrinsic fluorescence of several types of tissue, including Barrett s esophagus, 36 the uterine cervix, 8 and the oral cavity. 12 For these studies, fluorescence and reflectance spectra were acquired using a FastEEM instrument, depicted schematically in Figure This instrument allows collection of 11 fluorescence emission spectra excited between 337 and 610 nm and a white light (350 to 750 nm) reflectance spectrum in less than 1 s. Light is delivered to the tissue via an optical fiber probe, 38 which consists of a central light delivery fiber surrounded by six light collection fibers (all fibers have a 200- m core diameter and a numerical aperture (NA) of 0.22). At the tip of the probe, all fibers are fused together, creating an optical shield approximately 1.5 mm in diameter. The shield is beveled at a 17 angle to eliminate detection of specular reflections. In addition, the shield provides a fixed geometry for light delivery and collection, which are identical for the measured fluorescence and reflectance spectra. Xe Flash Lamp Spectrograph N 2 Laser Rapidly spinning dye cell wheel Detector Tissue Probe cross-sectional view FIGURE 31.1 Schematic representation of FastEEM instrument used to acquire fluorescence and reflectance tissue spectra in vivo. (From Georgakoudi, I. et al., Gastroenterology, 120, 1620, With permission.)

5 Extraction of intrinsic fluorescence spectra for in vivotissues has allowed minimization of the variations in line shape and intensity in measured fluorescence spectra that result from the presence of physiological blood content variations. 12 Insome cases, it is found that even significantly different measured fluorescence spectra correspond to similar intrinsic fluorescence spectra, mainly as a result of the removal of absorption distortions (Figure 31.2). Significant differences have been detected in the intrinsic fluorescence spectra of normal and diseased tissues in Barrett s esophagus, the uterine cervix, the oral cavity, and coronary arteries. Mean intrinsic fluorescence spectra for normal and diseased tissues based on the analysis of data from multiple patients are shown in Figure In contrast to the measured fluorescence spectra, which consist of nonlinear contributions from tissue fluorescence, scattering and absorption, intrinsic fluorescence spectra are composed of a linear combination of the fluorescence spectral features of the chromophores excited at a particular wavelength. Thus, once the component spectral features are identified, a simple linear decomposition can be performed to extract quantitative tissue biochemical information. Biochemical decomposition of measured tissue fluorescence spectra has been performed previously using the spectral features of commercially available chromophores diluted in saline. 5,18 However,because fluorescence is sensitive to the local environment of the chromophore, it does not necessarily follow that such spectra are an accurate representation of the chromophores spectral features in vivo. To extract the fluorescence signatures of two of the major tissue chromophores, namely collagen and NAD(P)H, in an in vivo environment, fluorescence EEMs and reflectance spectra were acquired during asphyxiation (i.e., elimination of blood flow) of human esophageal tissue in vivo. 39 The changes in the tissue redox state that take place during loss of oxygen were evident in the measured tissue reflectance spectra, and they were accompanied by spectral changes in the intrinsic tissue fluorescence. These changes were expected because the levels of NAD(P)H should increase as the levels of tissue oxygen decrease. The observed changes in intrinsic fluorescence during tissue deoxygenation could be described accurately by two spectral components extracted by analysis of the intrinsic fluorescence using a multivariate curve resolution (MCR) algorithm. 39 The fluorescence spectral features of these two components are consistent with those of collagen (commercially available collagen Type I) and NAD(P)H (from isolated cervical tissue epithelial cells). 39 Thus, it could be concluded that the MCR extracted fluorescence EEMs (Figure 31.4) represent the spectral signatures of collagen and NAD(P)H in vivo. A linear combination of the in vivo NAD(P)H and collagen EEMs extracted from the tissue asphyxiation measurements was fit to intrinsic fluorescenceexcitation emission matrices of normal and diseased tissues. This decomposition provided quantitative information about the biochemical make-up of tissue and the changes that take place during the development of disease. The results of this decomposition for Barrett s esophagus, cervical, oral, and coronary artery tissue sites are included in Figure Note that no normalizations were performed prior to biochemical decomposition of these data sets. Barrett s esophagus is defined as the replacement of normal squamous esophageal tissue by metaplastic columnar epithelium. Patients with Barrett s eosphagus are at higher risk for developing adenocarcinoma of the esophagus. As a result, they undergo regular endoscopic surveillance procedures in an attempt to detect changes at the precancerous or dysplastic stage when treatment can be effective. Unfortunately, dysplastic changes are endoscopically invisible. Thus, random biopsies are acquired, typically, one at each quadrant of the esophagus for every 2 cm of the Barrett s segment. A technique that would allow the physician to detect precancerous lesions or to serve as a guide to biopsy could improve significantly the clinical management of these patients. When a linear combination of NAD(P)H and collagen fluorescence spectra was fit to in vivo fluorescence spectra from seven patients with Barrett s esophagus, it was found that high-grade dysplastic tissues have lower levels of collagen and increased levels of NAD(P)H when compared to nondysplastic tissues. This information is diagnostically useful, and it provides important insights about the biochemical changes that occur in tissue during the development of premalignancies. For example, the increase in NAD(P)H for high-grade tissues is consistent with cellular hyper-proliferation or increased metabolic activity. 23 The decrease in collagen fluorescence could be the result of degradation in the collagenous network of the connective tissue. This in turn could be the result of an increase in the activity of

6 2.5 A measured fluorescence (a.u.) wavelength (nm) 0.35 B reflectance wavelength (nm) 10 C intrinsic fluorescence (a.u.) wavelength (nm) FIGURE 31.2 (A) Measured fluorescence spectra from two normal oral epithelial tissue sites, 337-nm excitation. (B) Corresponding measured reflectance spectra. (C) Corresponding intrinsic fluorescence spectra. Notice that the intrinsic fluorescence spectra exhibit much more similar spectral features than the corresponding measured spectra.

7 7 intrinsic fluorescence (a.u.) A intrinsic fluorescence (a.u.) B wavelength (nm) wavelength (nm) 12 C D intrinsic fluorescence (a.u.) intrinsic fluorescence (a.u.) wavelength (nm) wavelength (nm) FIGURE 31.3 Mean intrinsic fluorescence spectra from (A) ten nondysplastic (solid line) and five high-grade dysplastic lesions (dashed line) in seven Barrett s esophagus patients. (B) 43 normal squamous (solid line), 12 biopsied squamous metaplastic (dashed line), and 10 biopsied high-grade squamous intraepithelial lesions (dotted line) of the cervix from 35 patients. (From Georgakoudi, I. et al., Am. J. Obstet. Gynecol., 186, 374, With permission). (C) 28 normal (solid line), 8 dysplastic (dashed line), and 9 cancerous (dotted line) oral epithelial tissue sites from 15 patients. (D) 22 normal/intimal fibroplasia (solid line) and 88 atherosclerotic and atheromatous (dashed line) coronary artery tissue sites. All spectra were acquired in vivo with the exception of the coronary artery spectra. Excitation wavelength = 337 nm. collagenases, enzymes that cleave collagen. Indeed, an increase in the level of serine and cysteine proteases has been found in gastric and colorectal cancerous and precancerous lesions. 40 Such proteases are known to be activators of matrix metalloproteinases, a prominent class of tissue collagenases. 41 The decrease in collagen fluorescence could also be at least partially attributed to an increase in epithelial thickness, which would limit the amount of light that reaches the underlying stroma. Similar differences are found in the NAD(P)H and collagen fluorescence levels of normal and precancerous uterine cervical tissue sites. 8,39 During the normal reproductive life of a woman, squamous epithelium, the tissue type lining the ectocervix (i.e., the vaginal portion of the cervix), starts gradually to replace the columnar epithelium of the endocervix (i.e., the cervical canal that leads to the uterus). This replacement is known as squamous metaplasia and it takes place within the transformation zone. Most precancerous and cancerous cervical changes occur at the transformation zone. During colposcopy, the cervix is visualized under 6 or 15 magnification and colposcopically abnormal tissues are biopsied and examined histopathologically to determine the presence or absence of disease. Although colposcopy and biopsy are highly sensitive techniques for detecting cervical cancerous and precancerous lesions known as SILs, they have very low specificity. That means that a significant number of tissue sites that appeared colposcopically abnormal are histopathologically normal and did not have to be biopsied. A technique that would improve upon the specificity of colposcopy would enhance its efficiency and have a significant impact on the time and resources dedicated to these procedures.

8 0.15 intrinsic fluorescence (a.u.) nm 337 nm 380 nm A wavelength (nm) intrinsic fluorescence (a.u.) nm 380 nm 337 nm B wavelength (nm) FIGURE 31.4 (A) Intrinsic fluorescence spectra of tissue collagen extracted for 337-, 358-, and 380-nm excitation from measured fluorescence and reflectance spectra acquired during asphyxiation of human esophageal tissue in vivo. (B) Corresponding tissue NAD(P)H intrinsic fluorescence spectra. Especially in the case of the cervix, a tool that could provide a diagnosis in real time would potentially allow the combination of detection and treatment of a lesion during a single patient visit, providing a further positive economic and psychological impact. Results of the analysis of intrinsic fluorescence spectra acquired in vivo from colposcopically normal and colposcopically abnormal cervical tissue sites from 35 patients are shown in Figure 31.5B. Colposcopically abnormal tissue sites were biopsied and classified either as squamous metaplastic (i.e., benign) or SIL (i.e., precancerous). A significant decrease in collagen fluorescence was present between colposcopically normal and abnormal tissues. 8,39 Asin the case of Barrett s esophagus, this decrease could be the result of collagenases. Indeed, it has been reported that differences can be found in the levels or patterns of expression of metalloproteinases in normal, squamous metaplastic and SIL cervical sites. 42 As mentioned previously, most of the colposcopically abnormal tissue sites are found within the transformation zone of the cervix, an area of constant dynamic change. Higher levels of collagenase expression have been reported for tissues undergoing architectural changes as in tissue regeneration and wound healing. 43 In addition, we find that among the colposcopically abnormal tissues, SILs tend to have higher NAD(P)H fluorescence than squamous metaplastic sites. This increase could be attributed to increases in epithelial thickness or metabolic activity, as in the case of Barrett s esophagus. As indicated by the diagnostic threshold lines drawn based on logistic regression analysis, the colposcopically abnormal but histopathologically benign squamous metaplastic tissue sites can be separated fairly well from the

9 A B NAD(P)H 4 2 NAD(P)H C collagen 0 D collagen NAD(P)H ceroid collagen % relative collagen FIGURE 31.5 NAD(P)H and collagen fluorescence contributions to intrinsic tissue fluorescence excitation emission matrices (excitation = 337 to 425 nm; emission = 375 to 700 nm) of the patient populations described in Figure (A) Nondysplastic (squares) and high-grade dysplastic (circles) Barrett s esophagus. (B) Normal squamous epithelium (squares), colposcopically abnormal squamous metaplastic, i.e., benign (triangles) and high-grade SILs (circles) of the uterine cervix. (C) Normal squamous (squares), dysplastic (triangles), and cancerous (circles) nonkeratinizing oral epithelial tissues. (D) Ceroid fluorescence extracted from analysis of intrinsic fluorescence spectra excited at 480 nm shown as a function of the percentage of the relative collagen contribution with respect to the sum of the collagen and elastin fluorescence contributions to intrinsic fluorescence spectra excited at 337 nm for normal/intimal fibroplasia (squares) and atheromatous/atherosclerotic (circles) coronary artery. The decision lines in (B) were drawn based on logistic regression analysis. ([A] and [B] from Georgakoudi, I. et al., Cancer Res., 62, 682, With permission.) histopathologically abnormal tissues, illustrating how quantitative biochemical information can be valuable in helping the physician decide which colposcopically abnormal tissue sites to biopsy. A third tissue type for which intrinsic fluorescence spectra have been extracted and biochemically analyzed is oral squamous epithelium. Squamous cell carcinoma of the upper aerodigestive tract continues to be a major public health problem worldwide. In the U.S. alone, 40,000 new cases and 11,000 deaths wereexpected for the year Despite advances in radiotherapy, chemotherapy, and surgery, patients survival with oral cancer has not improved significantly. The entire oral cavity is lined with squamous epithelium; however, the detailed morphology and architecture of the tissue vary depending on its function. Initial studies indicate that it is diagnostically important to consider separately the intrinsic fluorescence properties of keratinized and nonkeratinized epithelia. 12 The NAD(P)H and collagen contributions to the intrinsic fluorescence of normal, dysplastic and cancerous nonkeratinized epithelial sites are shown in Figure 31.5C. A decrease in collagen and an increase in NAD(P)H fluorescence are observed

10 in dysplastic and cancerous oral tissues, consistent with the changes observed in cervical and Barrett s esophagus tissues. Finally, intrinsic fluorescence of coronary artery, a nonepithelial tissue, has been extracted and decomposed into fluorescent biochemical constituents. Despite major improvements in diagnosis and treatment, coronary artery disease is still the most significant cause of death in the United States, and is responsible for more than 3 million hospital admissions every year. 45 Coronary angiography, and more recently intravascular ultrasound, are the most widely used methods for invasive diagnosis of coronary artery disease. However, the sensitivity of these tools in identifying the morphological components of atherosclerotic plaques, and especially of necrotic core, is poor. Laser-induced fluorescence (LIF) spectroscopy has been tested in the last decade in the in vitro diagnosis of atherosclerosis, with the purpose of improving the morphology-based diagnosis of the plaque. 17,46,47 Four chemical components with specific fluorescence spectral features have been identified in the human arterial wall: collagen, elastin, ceroid, and tryptophan. 17,46 48 Collagen and elastin are the structural proteins of normal and diseased arteries. An increase in the collagen content of atherosclerotic tissues has been reported Tryptophan is an amino acid present in the skeleton of numerous proteins in the intimal extracellular matrix that is increased in atherosclerotic plaques. Ceroid is an insoluble conglomerate with marked autofluorescence properties, present in the atherosclerotic macrophage cells and necrotic core. 52 Oxidized low-density lipoprotein is its main chemical component, which gives rise to a characteristic spectral fingerprint of the atherosclerotic lesion. 46,52,53 After being harvested from cardiac transplant patients and postmortem autopsies, 110 coronary artery segments were investigated ex vivo with the FastEEM. While extracting the intrinsic fluorescence of normal and diseased coronary artery tissues, it was found that beta carotene, a second tissue absorber in addition to hemoglobin, was affecting significantly the line shapes of the measured reflectance and fluorescence spectra. Once beta carotene absorption was incorporated in the analysis of the fluorescence and reflectance spectra, intrinsic tissue fluorescence excited at 340 and 480 nm was extracted and decomposed biochemically into contributions from collagen, elastin, and ceroid. The basis spectra for these components were derived based on the fluorescence characteristics of commercially available elastin and collagen type I and MCR. A significant increase was found in the ceroid and collagen fluorescence of atheromatous and atherosclerotic specimens when compared to that of normal and intrimal fibroplasia tissues, consistent with the histopathological analysis of these specimens (Figure 31.5D). In conclusion, in this section we summarized methodologies that can be used to extract quantitative tissue biochemical information based on fluorescence spectroscopic measurements. Removal of distortions introduced in measured tissue fluorescence by scattering and absorption is essential in achieving this goal. The extracted quantitative information can be diagnostically useful, providing important insights into the biochemical changes that take place in vivo during disease development. In addition, such information can be used to optimize the design of instruments to detect or monitor lesions based on fluorescence Characterization of Bulk Tissue Optical Properties Using Diffuse Reflectance Spectroscopy in Vivo A diffuse reflectance spectrum arises from light that has been scattered multiple times within the sample of interest. The spectral features of light diffusely reflected from tissue depend on its scattering and absorption properties. Diffuse reflectance spectroscopy studies the changes in these optical properties associated with disease or therapy. Reflectance measurements can be performed with very short light pulses (time-domain), an intensity-modulated source (frequency-domain), or a steady-state broadband or monochromatic source. In this section, we will focus on the use of visible steady-state in vivo diffuse reflectance measurements to characterize tissue and provide clinically useful diagnostic information. A broad review of elastic light-scattering spectroscopy and diffuse reflectance is provided in Chapter 29 of this handbook.

11 A number of clinical studies performed with tissues such as colon, 54,55 bladder, 56 breast, 57 ovary, 58 and skin demonstrate that diffuse reflectance spectra contain diagnostically useful information. Typically, an optical fiber probe is used to deliver white light from a high-power lamp onto the tissue and to collect the diffuse reflectance. Several algorithms have been developed based on statistical or on empirical approaches. For example, the ratio of the area under the normalized reflectance curve between 540 and 580 nm to the area under the reflectance between 400 and 420 nm has been used to differentiate neoplastic from nonneoplastic colon tissues. 64 The slope of the reflectance in the 330- to 370-nm range was employed to distinguish between malignant and nonmalignant bladder tissues. 56 A combination of parameters related to the area under the reflectance curve, the reflectance slope and the mean intensity at specific wavelength bands were used by Wallace et al. 65 to distinguish between malignant melanoma and benign pigmented skin lesions. Such skin lesions were also separated with 80% sensitivity and 51% specificity using an imaging approach that combined reflectance-related parameters with morphological features of the lesion, such as dimension and roundness. 62 Neural network-based pattern recognition algorithms have been also used to identify characteristic skin, 63 breast, 57 and colonic 54 neoplastic reflectance features. A semiempirical model developed to describe skin optical properties based on specific features of the reflectance was reported by Dawson et al. 60 In this model, skin was treated as a four-layer system with absorption due to fibrous protein, melanin, and hemoglobin in the first three layers, respectively, and scattering due to collagen and fat in the fourth layer. By assuming that the amount of reflected light from the first three layers is minimal, they found that the logarithm of the inverse skin reflectance (LIR) is equal to the sum of the absorbances of the first three layers minus the log of the reflectance of the fourth layer. The effects of scattering were empirically accounted for in the LIR spectrum by subtracting a line connecting the LIR intensities at 510 and 610 nm. Using the LIR values at 510, 543, 560, 576, and 610 nm, an erythema index was defined that was proportional to the hemoblobin content of the skin sample. An approximate correction for melanin scattering had to be implemented to ensure that the erythema index was not correlated with the pigmentation or melanin content of the skin. The melanin index was characterized empirically by using the slope of the LIR spectrum between 650 and 700 nm. However, this erythema index was dependent on the oxygen saturation levels of hemoglobin. Feather et al. 66 showed that the hemoglobin index, which depended on the gradients of the LIR values at four isosbestic points between and 573 nm, was independent of oxygen saturation and, thus, a more suitable parameter for quantifying the amount of cutaneous hemoglobin. Skin oxygen saturation levels could be estimated by combining the hemoglobin index with the LIR intensity at nm. These empirical expressions for hemoglobin content and oxygen saturation were validated with in vitro measurements of red cells in plasma. To perform similar measurements in vivo, it was necessary to remove significant contributions from specular reflections at the skin surface. This was achieved by using crossed linear polarizers for light delivery and detection. 61 Further empirical modifications were also implemented to correct for hemoglobin absorption effects in the case of the melanin index, and for melanin absorption and scattering in the case of the hemoglobin index. The usefulness of these models for characterizing the optical properties of skin was illustrated by analyzing in vivo measurements performed with different skin colors and on a human forearm raised to different heights relative to the heart. However, there are no reports for this latter model being used clinically as an aid in detecting or treating skin lesions. Another empirical model based on analysis of the LIR spectrum has been reported by Koenig et al. 67 and used to describe reflectance spectra collected from the bladder 67 and the colon. 54 First, measured reflectance spectra were converted into absorbance units using the expression, A = log(r/ro), where R and Ro were reflectance from tissue and a white reflectance standard, respectively. Then, a line was fit to the 640- to 820-nm range of the spectrum. This line was assumed to represent contributions from tissue scattering to the absorbance spectrum and was subtracted after it was extrapolated to cover the entire absorbance spectrum. Hemoglobin oxygen saturation was estimated based on the absorbance intensities at 555 and 577 nm, which are peak absorption wavelengths for deoxy- and oxyhemoglobin, respectively. The total amount of blood was estimated in the case of the bladder study based on these intensities and was found to be the most diagnostically useful parameter for discriminating neoplastic

12 from nonneoplastic bladder tissue with high sensitivity (91%), but low specificity (60%). 67 The high blood content of inflammed tissues was thought to be the reason for the low specificity results. This model was also used to interpret the success of statistically based algorithms designed to differentiate between neoplastic from neoplastic colon tissues, as well as between adenomatous and hyperplastic colon polyps, which is the more clinically relevant problem in the colon. 54 However,the extracted parameters were not used as the basis of a diagnostic algorithm. Zonios et al. 55 have developed a more theoretically rigorous model describing the measured tissue reflectance as a function of the absorption and reduced scattering coefficients for a light delivery/collection geometry consistent with that of an optical fiber probe. This model is based on an expression developed by Farrell et al., 68 which describes the diffuse reflectance from a narrow pencil beam of light incident on the surface of a semi-infinite turbid medium in terms of the reduced scattering ( s ) and absorption ( a ) coefficients and the source-collection fiber separation distance. The latter expression was derived using the diffusion approximation to the light transport equation. To acquire an analytical expression, Zonios et al. assumed point delivery of light and collection over a circular spot with an effective radius, extracted from the model, approximately equal to the radius of the optical fiber probe. This model is particularly appropriate for tissues that can be modeled by a single layer. Some a priori knowledge with regard to the spectral features of the tissue s scattering and absorption is also required before using this model to describe tissue reflectance spectra. This is needed because the measurements employ a probe with a single source-collection fiber separation; hence, only a single piece of information is at each wavelength, insufficient to extract the diagnostic parameters, s and a, independently. In particular, the identity of the absorbing tissue chromophores and their corresponding tissue spectra must be known. In most cases, this does not present a serious limitation. For example, in the case of most epithelial tissues, oxy- and deoxyhemoglobin, whose extinction coefficients have been studied widely, are the only significant absorbers in the visible region of the spectrum. In the case of other tissues such as breast and artery, additional absorbers, such as beta carotene, need to be included. Melanin absorption also needs to be included for tisssues such as skin. The level of agreement between the observed reflectance and the model fit can be used to ascertain whether all the absorbers have been accounted for. This model has been used to describe, with good agreement, reflectance spectra acquired in vivo from tissues such as the colon, 55 Barrett s esophagus 36 (Figure 31.6A), the cervix, 8 the oral cavity, 12 the artery (Figure 31.6B), and the breast. Notice the differences in the reflectance spectral features of Barrett s mucosa and coronary artery tissue, attributable mainly to the presence of beta carotene absorption in coronary artery. Based on this model analysis, quantitative information was extracted with regard to the bulk tissue scattering and absorption properties. For example, the extracted total hemoglobin concentration was significantly higher for adenomatous polyps than for normal colon mucosal tissues, while no significant changes in tissue oxygenation were detected. 55 Coronary artery tissue classified histopathologically as normal or as intimal fibroplasia exhibits consistently low levels of beta carotene absorption, in contrast to calcified and noncalcified atheromatous and atherosclerotic coronary arteries, which have varying amounts of beta carotene (Figure 31.6D). In addition, the reduced scattering coefficient of colon polyps was generally lower and varied less as a function of wavelength than that of normal colon mucosa. This is a diagnostically useful trend that has also been detected between dysplastic and nondysplastic tissues such as Barrett s esophagus, 36 the cervix, 8 and the oral cavity. 12 To quantify these changes, the slope and intercept of a line fit to the wavelengthdependent s was used. The gradual decrease in the slope and intensity of s as Barrett s esophagus tissue is transformed from nondysplastic to low-grade and high-grade dysplasia is shown in Figure 31.6C. This decrease in the reduced scattering coefficient could be due to an increase in epithelial thickness, which in turn leads to less light reaching the highly scattering collagen fibers of the stroma; thus, less light is reflected within the collection angle of the fiber probe. In addition, changes in the density of the collagen fibers resulting from an increase in the activity of collagenases, as discussed in the fluorescence section, could also contribute to the observed decrease in the reduced scattering coefficient.

13 A B reflectance reflectance C 0.01 reduced scattering coefficient slope (-mm -1 λ -1 ) 1E wavelength (nm) beta carotene concentration (a.u.) D E wavelength (nm) 1 10 reduced scattering coefficient intercept (mm -1 ) sample number FIGURE 31.6 Measured reflectance spectra (solid lines) from a nondysplastic Barrett s esophagus tissue site (A) and an atheromatous coronary artery tissue sample (B) with corresponding fits (dashed lines). (C) Slope and intercept of line fit to the wavelength-dependent reduced scattering coefficient for nondysplastic (squares), low-grade (diamonds), and high-grade (circles) dysplastic Barrett s esophagus tissues. (D) Beta-carotene concentration extracted from the absorption coefficient of normal/intimal fibroplasia (squares) and atheromatous/atherosclerotic (circles) coronary artery. ([A] and [C] from Georgakoudi, I. et al., Gastroenterology, 120, 1620, With permission.) These results demonstrate that diffuse reflectance spectroscopy is a promising tool that can be used clinically to detect and quantify changes in tissue biochemistry and morphology. Further improvements in the models used to describe measured tissue diffuse reflectance spectra, that would account more rigorously for the layered tissue architecture 69,70 and small source-detector separations, 71,72 could enhance the sensitivity of these measurements and allow the detection of more subtle changes with higher accuracy Characterization of Cellular and Subcellular Morphology Using Light-Scattering Spectroscopy Light-scattering spectroscopy (LSS) is a novel optical technology for imaging and characterizing the morphology of subcellular organelles within the epithelial linings of the body. 73,74 LSS is based on the fact that the spectral and angular distributions of light scattered by a particle depend on the size, shape, and internal structure of this particle. Thus, by analyzing light that has undergone a single backscattering event in tissue, we can extract quantitative morphological information about the scattering particle. As discussed next, the backscattering angle at which light is collected can be adjusted to gain some selectivity in terms of the size of the scattering particle that is characterized spectroscopically. For example, scattering in almost the exact backward direction provides detailed information about the cell nucleus. 8,36,73 75 Changes in the morphology of the nucleus, such as enlargement, pleomorphism (variation in size and

14 shape), crowding (increase in the number of nuclei per unit area), and hyperchromatism (increase in chromatin content or nuclear material) comprise important histopathologic hallmarks for the diagnosis of precancerous and cancerous lesions; thus, this technique bears great promise as a noninvasive tool for characterizing important tissue morphological features and for detecting disease. Light scattering measurements are performed routinely for extracting cell size in flow cytometry. 76 Goniometric light scattering studies, i.e., studies that examine light scattering as a function of scattering angle, have also been performed in the 2- to 170 -range with suspensions of cells and subcellular organelles to characterize their scattering properties Unfortunately, single scattering events are masked in biological tissue, because only a small portion of the light incident on tissue is returned after a single scattering. 81 The rest enters the tissue and undergoes multiple scattering from a variety of tissue constituents. As a result, it becomes randomized in direction, producing a large background of diffusely scattered light. (This is the diffuse reflectance component discussed in the previous section.) This diffuse component is influenced significantly by hemoglobin absorption and scattering by cells, as well as by noncellular structures, such as collagen. As discussed earlier, important information can be obtained about the overall tissue optical properties from analysis of the diffuse reflectance spectra. In this section, we focus on the light representing single backscattering events. To study this, the diffusely scattered component must be removed from the overall reflected light. Several approaches may be used to accomplish this. One employs a mathematical model to describe the diffusive background. Examples of such models were described in the previous section. The diffuse reflectance model fit is subtracted from the overall measured reflectance spectrum to obtain the single scattering component. Polarized light can be used to accomplish this as well. 77,82 This approach uses the fact that incident light singly scattered in the backward direction retains its polarization, whereas multiply scattered light becomes depolarized. Thus, by using linearly polarized incident light, the contribution due to single scattering can be obtained as the difference between the components of light scattered from the tissue polarized parallel and perpendicular to the direction of polarization of the incident light. Recent measurements on tissue phantoms consisting of water and polystyrene beads using a single 200- m core diameter fiber for light delivery and collection indicate that such a probe geometry could also be sensitive to the size distribution and refractive index of the scattering particles near the surface of the sample. In addition, this study suggests that determination of the size and refractive index in this case could be achieved simply by studying the features of the intensity oscillations of the collected light (i.e., background removal would not be required). 83 The spectrum of the single scattering component can be analyzed to obtain information about the properties of the scatterers. 77,82,83 The origin of LSS signals depends on the geometry of collection. For example, particles that are large compared to the wavelength have strong scattering components in the near-exact forward and backward directions (even though backscattering is much weaker than forward scattering). 84 Cells and nuclei have the right size to exhibit this type of scattering behavior. Early light scattering measurements performed with suspensions of HeLa cells, Chinese hamster oocytes, and white blood cells indicated that the detected scattering patterns were in good agreement with Mie scattering from a dense sphere embedded within a larger softer sphere. 85 However, in tissue, because cells are adjacent to one another, there is index matching among the cell membranes of neighboring cells; thus, the nucleus becomes the major large size scatterer. In addition, the intensity of the backscattered light from large particles oscillates in intensity as a function of wavenumber with a frequency characteristic of the particle size and relative refractive index. 84 Particles that are small compared to the wavelength, such as the tubules of the endoplasmic reticulum, scatter light in an approximately isotropic fashion over all angles. The light-scattering intensity as a function of wavelength also exhibits very broad features. 84 The angular and wavelength-dependent distributions of the light intensity scattered by particles whose size is comparable to the wavelength, such as mitochondria and lysosomes, exhibit significantly broader features than the corresponding spectra of large particles but not as broad as those of small particles. 84 For cells with a high volume fraction of mitochondria, such as hepatocytes, the scattering properties are dominated by those of mitochondria, even in the exact backward direction. 86 When particles of several sizes are present, the resulting signal is

15 a superposition of these variations. Thus, the size distribution and refractive index of the scatterers can be determined from analysis of the spectrum of light backscattered by these particles. Once the size distribution and refractive index are known, quantitative measures characterizing alterations of morphology of the epithelial cells can be obtained, and corresponding diagnostic algorithms can be developed. Preliminary in vivo studies have been performed to assess the potential of this technique as a tool for detecting precancerous and early cancerous changes in five different organs with three different types of epithelia: columnar epithelia of the colon and Barrett s esophagus, 36,73,75 transitional epithelium of the urinary bladder, 73 and stratified squamous epithelium of the oral cavity 35,73 and the uterine cervix. 8 Reflectance spectra were collected using the FastEEM instrument described in Section The diffuse background was subtracted from measured reflectance spectra using modeling. 55 The light scattering spectra were analyzed based on the van de Hulst approximation for light scattering by particles that are large compared to the wavelength. 74 The extracted size distributions were then used to obtain quantitative measures characterizing the degree of nuclear enlargement and crowding. Figure 31.7 displays these LSS parameters in binary plots to show the degree of correlation with histological diagnoses. In all five organs there is a clear distinction between dysplastic and nondysplastic epithelium. Both dysplasia and CIS exhibit a higher population density of nuclei than normal tissues and either a higher percentage of enlarged nuclei (Figure 31.7A through D) or a larger variation in the distribution of nuclear size. These features can be used as the basis for spectroscopic tissue diagnosis. Studies of backscattering of linearly polarized light from cells, 77,82 ex vivo 77,82,87 90 and in vivo 77 tissues have also shown great promise for characterizing cell and tissue morphology. Experiments have been performed during which the backscattered light collected from a small tissue area (approximately 3 mm 2 ) was detected along the parallel and the perpendicular polarization relative to the propagation of the incident light. 82 The light-scattering spectrum extracted by subtraction of the perpendicular from the parallel polarized light was analyzed to provide information about the size distribution of cell nuclei from cell monolayers and ex vivo tissues. Significant differences were detected in the nuclear size distributions from normal and tumor colon tissues, consistent with the results of histopathology from corresponding thin tissue sections. 82 Further measurements with tissue phantoms and ex vivo and in vivo tissues have been performed with a probe with a linear pollarizer at its tip, which consisted of a central light delivery fiber and four light collection fibers positioned symmetrically around the light collection fiber. 77 These studies concluded that the spectral features of backscattered light from a biological sample with the same polarization as the incident light are dependent on the internal morphology of the cell. LSS measurements have also been performed in an imaging modality using polarization for background removal. Typically, the sample is illuminated with linearly polarized light, and the backscattered light is collected along the parallel and perpendicular polarizations. Jacques et al. 88 showed that, by combining the information in these two images, one can acquire an image sensitive only to the morphology of the upper few hundred micrometers of skin, where most skin cancers start to develop. In a recent study by Gurjar et al., 90 it was shown that, by imposing limitations on the angle of the backscattered light collected, quantitative information can be acquired about the size distribution and refractive index of epithelial cell nuclei (Figure 31.8). A cm area was illuminated with narrowband collimated linearly polarized light. Light backscattered within one degree from the exact backward direction along the parallel and perpendicular polarizations with respect to the incident light was imaged onto a charge-coupled device (CCD) camera. By subtracting the perpendicular image from the parallel-polarized image pixel by pixel, the singly backscattered light intensity was extracted. A series of such images was acquired for 13 narrow wavelength bands, such that a light-scattering, wavelengthdependent spectrum could be constructed for each CCD pixel. These spectra were analyzed using Mie scattering theory to determine the corresponding distributions of nuclear size and refractive index. While the spatial resolution of these images is limited by the CCD pixel size, the size distribution of the nuclei within that pixel can be determined with an accuracy of 0.1 m, and the corresponding refractive index can be estimated with three-digit accuracy. 90 LSS images representing distinct regions of nuclear enlargement within the physical outline of an adenomatous colon polyp are shown in Figure Similar maps have been obtained to represent an increase in the relative

16 total number of nuclei 100 per mm 2 total number of nuclei 100 per mm A % of enlarged nuclei C % of enlarged nuclei total number of nuclei 100 per mm 2 total number of nuclei 100 per mm B % of enlarged nuclei D % of enlarged nuclei total number of nuclei 100 per mm E nuclear size std (µm) FIGURE 31.7 The population density of nuclei is plotted against the percentage of enlarged nuclei (i.e., nuclei larger than 10 m) for (A) Barrett s esophagus epithelium (squares, nondysplastic Barrett s mucosa; triangles, low-grade dysplasia; circles, high-grade dysplasia). (B) Colon epithelium (squares, normal colon mucosa; circles, adenomatous polyp). (C) Urinary bladder epithelium (squares, normal cells; circles, transitional cell carcinoma in situ). (D) Oral cavity epithelium (squares, normal cells; triangles, low-grade; circles, squamous cell carcinoma in situ). (E) Uterine cervical epithelium (squares, normal and squamous metaplastic epithelium; circles, SILs). Note that the standard deviation of the nuclear size distribution is plotted in the x axis as opposed to the % enlarged nuclei in (E). ([A] through [D] from Backman, V. et al., Nature, 406, 35, [E} from Georgakoudi, I. et al., Am. J. Obstet. Gynecol., 186, 374, With permission.)

17 Polarizer Mirror Source Filters CCD Beam Splitter Polarizer Tissue FIGURE 31.8 Schematic diagram of instrument used for acquiring images with polarized LSS. (From Gurjar, R. et al., Nat. Med., 7, 1245, With permission.) mm Adenoma Non-dysplastic mucosa FIGURE 31.9 LSS image showing the spatial distribution of the percentage of enlarged nuclei, i.e., nuclei larger than 10 m, for an adenomatous colon polyp. (From Gurjar, R. et al., Nat. Med., 7, 1245, With permission.) refractive index of cell nuclei (i.e., hyperchromatism) within the adenomatous polyp. 90 Results of nuclear morphometry acquired in this fashion are consistent with hematoxylin and eosin stained thin sections prepared from the same polyp following acquisition of the LSS images. Wavelength-dependent, light-scattering spectra have also been collected as a function of angle (angular LSS) from monolayer cultures of benign mesothelial intestinal cells and T84 tumor colonic cells. 91 Analysis of these measurements using Mie theory shows that the cell nuclei scatter predominantly within a narrow cone (1 to 2 ) near the exact backward direction, in agreement with previous results as well as theoretical calculations (Figure 31.10). Thus, the signal measured within a sufficiently narrow solid angle provides information about the size and refractive index of the cell nuclei. The signal scattered into larger scattering angles (>3 ), particularly with 45 azimuth with respect to the polarization of the incident light, provides information about subcellular structure at the submicron scale (see Figure 31.10). In this angular regime, the LSS signals could not be fitted under the assumption that the particles are normally distributed for any given mean size from 0.3 to 15 m. Moreover, other types of size distributions, such as top-hat, exponential, or skewed-normal could not fit the data. The spectra were found to be best described by an inverse power-law distribution for the number of particles, where the concentration N(d) of particles with a diameter d,was given by N(d) d with the exponent not dependent mm Enlarged Nuclei, %

18 FIGURE Experiments with two-layer tissue models with the upper layer formed by a monolayer of T84 colon tumor cells. (A) A/LSS contour map at = 532 nm. (B) Spectra of light scattered by cells in regions A and B marked on map in (A). The analysis of these spectra enables extraction of quantitative characteristics of the size distributions of scattering particles responsible for the respective scattering spectra. (From Backman, V. et al., IEEE J. Sel. Top. Quantum Electron., 7, 887, With permission.) on d and characterizing the variations of refractive index inside the cells and, thus, the variations of intracellular density. To obtain the values of, a Mie theory-based inversion procedure was used to fit the data. The sizes were assumed to be distributed between 0.01 and 1.5 m according to an inverse power law, with fitting parameter. For T84 tumor cells, was found to be approximately 2.2, whereas for normal mesothelial cells the best fit was obtained for = 2.7. Thus, the size distribution of the T84 colon tumor cells is shifted with respect to that of the normal cells, with an increase in the relative number of large intracellular and intranuclear structures. We note that an inverse power-law distribution is a hallmark of fractal behavior. 92 Inverse power-law distributions have been used previously to describe refractive index variations in frozen tissue sections 93,94 and have been associated with the fractal dimension of tissue. The smaller values of the size exponent for malignant cells can be correlated with certain alterations of normal cell structure associated with cancerous changes. For example, a smaller value of in the size distribution of the intranuclear structure correlates with the visual perception of clumped and rough chromatin when a stained tissue sample is microscopically evaluated. Pathologists evaluating hematoxylin and eosin stained samples of precancerous cells frequently observe such changes. Smaller values of for malignant cells may also indicate higher structural entropy and, therefore, higher disorganization of the cell structure. Finally, a difference in may be related to change in the fractal properties of the malignant cells. Thus, angular LSS studies can provide information about the organization and packing of subcellular and subnuclear structures at scales significantly more sensitive than conventional microscopic imaging. These initial studies demonstrate that LSS can be used not only to detect changes in nuclear morphology that are well established hallmarks of precancerous and cancerous lesions but also to characterize subtle changes in the morphology and organization and packing of cells and tissues that have not been explored previously Characterization of Tissue Molecular Composition Using Near-Infrared Raman Spectroscopy Near-infrared (NIR) Raman spectroscopy is well suited for probing the detailed biochemical and morphological composition of human tissue Unlike fluorescence, reflectance, and NIR absorption spectroscopies, Raman spectroscopy provides narrow spectral bands with high information content that can

19 be assigned to specific molecular vibrations. Whereas relatively few endogenous biological fluorophores exist, many of whose spectra significantly overlap, there exist a multitude of Raman active chemicals in tissue that have unique spectra and can be specifically related to healthy and diseased conditions. Several features of NIR Raman spectroscopy are especially important to the study of human tissue. NIR excitation wavelengths have relatively small extinction coefficients and large penetration depths in human tissue (approximately 1 mm), providing the opportunity to observe subsurface structures. The small absorption coefficient also precludes photolytic sample decomposition. Additionally, in contrast to vibrational spectra obtained via mid-ir absorption, water is a relatively weak absorber in the NIR, and water interference is not a problem in Raman experiments. Furthermore, the strong fluorescence interference from biological tissue samples encountered with visible excitation wavelengths is significantly reduced in the NIR region, rendering Raman features more easily accessible. Most importantly, the sharp bands in the IR fingerprint region can be conveniently studied as Raman shifts and allow identification and quantification of the chemical species involved. Quantitative analysis is achieved by examining the intensities of molecular vibrational spectral bands because they provide a direct reflection of molecular concentrations. Such an analysis is possible because the intensity of the Raman bands for a given moiety is linearly related to its concentration. Several groups have investigated methods to employ the technique and accurately extract chemical concentrations The ability to quantitatively probe biochemical features that characterize normal tissue and accompany disease progression is of substantial importance in the utility of Raman spectroscopy as a diagnostic aid. Furthermore, in several diseases, prognosis is directly related to the concentration of key chemical constituents in the tissue. Although it is often difficult to determine the spectra of all the components of a Raman spectrum from a biological sample, due to its complexity, spectral analysis techniques can still be used to extract quantitative information. These techniques are known as multivariate analysis methods, or chemometrics, and can be implemented to extract chemical concentrations, even without complete knowledge of the chemical s Raman spectrum or cross section. 105,106 Although chemometric techniques are powerful algorithms in spectral analysis, the accuracy of a model built depends on the quality of the spectral data set and the accuracy of the reference measurements. Several geometries can be employed for quantitative Raman spectroscopy. These include direct backscattering, excitation at various angles relative to the collection optics, microscopic illumination; they can include free-space optics, fiber optics, or a combination of the two. The particular geometry invoked depends on the nature of the sample and the conditions required for the particular application. The common overlying factor involved in Raman spectroscopy instrumentation is that the system must have a high throughput and efficiency because of the inherently weak nature of the Raman effect. Two specific instruments used in the development of quantitative spectroscopic models are presented here as examples. They have spectral resolutions of 8 cm 1,which is sufficient to resolve all relevant Raman bands. The laboratory system used for studying biopsied biological tissue, shown in Figure 31.11A, is capable of collecting Raman and fluorescence spectra from macroscopic or from microscopic samples. 107 NIR Raman excitation at 830 nm is provided by an argon ion laser-pumped Ti:sapphire laser. The excitation laser beam traverses a bandpass filter and can be focused onto a bulk sample ( 1-mm diameter spot) or coupled into the confocal microscope ( 2- m diameter spot) via a prism that can be moved in and out of position. If bulk tissue is studied, the Raman-scattered light is collimated, filtered, and coupled into an imaging spectrograph attached to a CCD detector. For microscopic measurements, an epi-illuminated microscope is used. The microscope objective focuses the excitation light and collects the Raman scattered light in a backscattering geometry. A dichroic beamsplitter and mirror combination redirect the Raman-scattered light to the spectrograph system along the same optical path used for the bulk tissue system, after passing through a confocal pinhole to increase axial resolution. A CCD camera atop the microscope allows for registration of the focused laser spot with a white-light transilluminated image. Typically, a 63 infinity-corrected water immersion objective (0.9 NA) is used. The detector and the microscope translation stages are computer controlled; therefore, spectral maps of the tissue can be created by raster-scanning the translation stage under the microscope objective.

20 Argon ion pump laser Dichroic beam-splitter Ti: sapphire laser NIR excitation (830 nm) Notch filter CCD f/4 Spectrograph Confocal pinhole Band-pass filter CCD Camera Motorized translation stage Collimating lenses 830 nm Diode Laser Bandpass filter Cassegranian reflective objective Sample Coated prism Notch filter Collection fiber bundle Spectrograph CCD FIGURE (A) Schematic diagram of instrument designed to acquire fluorescence and Raman spectroscopy maps of thin samples and thick tissue specimens. (B) Instrument designed to acquired Raman spectra from blood samples. (From Berger, A.J. et al., Appl. Opt., 38, 2916, With permission.) Simply by changing the excitation source, the filters/beamsplitters, and spectrograph grating, the system can be adapted for collecting fluorescence. Three additional wavelengths, 352, 477, and 647 nm, are delivered to the system via optical fibers. The switch between Raman and fluorescence can be made in less than 2 min, thereby allowing collection of complementary data of point measurements or spectral maps. The experimental setup designed for the measurement of aqueous biological analytes using NIR Raman spectroscopy is shown in Figure 31.11B. 103 Excitation light, provided by an 830 nm diode laser, is bandpass filtered and delivered to the sample via a prism mounted in a Cassegrain microscope objective. The Cassegrain provides coaxial excitation with wide-angle collection and the backscattered Raman light from the sample is notch filtered and reimaged onto an optical fiber bundle. The circular collection fiber bundle is converted to a linear array and coupled directly into an imaging spectrograph. The light is dispersed onto a CCD detector and the fiber bundle image is binned to produce a spectrum. Raman spectra are often analyzed by looking at markers such as the ratio of the intensities of two Raman bands. Such approaches have been useful in developing diagnostic algorithms for diseases such as breast cancer and cervical cancer However, because the amount of information contained in Raman spectra is large and relevant to disease diagnosis and to the study of disease progression, it is prudent to develop spectroscopic models that take full advantage of this content. Methods that use information about the entire spectrum can be statistical or based on physical models. Statistical methods are used when the spectra of the individual tissue components are not accurately known. There are two distinct types of problems in Raman spectral diagnosis of biological tissue:

21 classification analysis ( Is this specimen in category A or B? ) and quantitative analysis ( How much of C is present? ); statistical methods can be applied to both. In the case of classification, statistical analysis is useful for proof-of-principle studies to evaluate the diagnostic capabilities of Raman spectroscopy when a new disease is being approached, or when there is an incomplete understanding of the basic constituents comprising the Raman spectra of the tissue so that a more physical model cannot be constructed. In this case, principal components are used to characterize the Raman spectra of a calibration set of samples containing A and B, as determined by pathology. As mentioned earlier, the principal components constitute an orthogonal set of spectra, a linear combination of which can accurately characterize each of the spectra in the sample set. By correlating the fit coefficients ( scores ) of the spectra in the data set with their known classifications, a diagnostic algorithm can be established. This approach has been shown to be effective in several tissue types, including atherosclerosis, breast cancer, and gastrointestinal cancers. 108,111,112 However, its drawback is that the principal components are mathematical constructs, without direct physical meaning. In the case of quantitative analysis of biological samples the procedure is similar, except that the calibration data set is composed of samples with various values of C, as determined by a standard reference technique. A variety of techniques to correlate the fit coefficients with the reference values for this type of problem include principal component analysis and partial least squares (PLS) analysis. An example of the use of PLS in determining the concentration of blood analytes is discussed next. Methods of analyzing tissue statistically, without full knowledge of the constituent spectra, are called implicit methods. On the other hand, if the spectra of the tissue constituents are accurately known, physically based explicit models can be developed. These can be based on the known chemical moieties in the tissue, its morphological constituents, or a combination of both. 107,113,114 Even if this approach cannot provide a complete characterization of all the spectral features from a given tissue type, it can usually account for the majority of them and subsequently lead to identification of more subtle, and yet important, constituents. The use of physical models is based on the linear nature of the Raman effect; this allows the leastsquares minimization reconstruction of the complex Raman spectrum of a heterogeneous macroscopic ( 1 mm 3 ) sample from a set of basis spectra obtained separately from the individual chemical or morphological constituents. Such a model is mathematically described as follows: R( ) r( ), i i i (31.1) where K is a constant that depends on the experimental system and collection geometry, R( ) is the Raman spectrum of the macroscopic sample, r i ( ) is the Raman spectrum of a given basis spectrum, and i is the concentration of the corresponding chemical or morphological component. If the basis spectra are of individual chemicals, they can be obtained microscopically or macroscopically, and the intensity of various spectral features can be directly related to concentrations by controlling the experimental conditions when the model is developed. If the basis spectra are of individual morphological features, a confocal microscope of the type described previously can be employed to collect spectra of various structures. The latter approach has the advantage that the spectra are observed in their native environment. Although the morphological approach can be quantitative, in practice quantification is difficult because concentrations cannot be directly measured. Several studies have used the morphological approach to date; 107,113,114 however, they are only semiquantitative. Different methods are appropriate for different applications. For example, in the case of measuring blood analytes, when chemical concentrations are derived, implicit methods are most appropriate. In other cases, such as breast or artery tissue analysis, when morphologic structures are characterized, explicit methods are the most appropriate. Below, we give examples of both. Combination methods that introduce physical information into statistical techniques, such as hybrid linear analysis, 115 are also possible. Statistical methods can be used to build an accurate quantitative spectroscopic model. One such example is the measurement of concentration of blood analytes, with an ultimate goal of obtaining

22 TABLE 31.1 Prediction Accuracy of Blood Analytes in Human Serum Using a PLS-Generated Algorithm Analyte Reference Integration Time Error RMSEP r 2 (s) Glucose 3 mg/dl 26 mg/dl Cholesterol 4 mg/dl 12 mg/dl Triglyceride 3 mg/dl 29 mg/dl BUN (urea) 0.9 mg/dl 3.8 mg/dl Total protein 0.1 g/dl 0.19 g/dl Albumin 0.09 g/dl 0.12 g/dl accurate transcutaneous measurements so that, for example, diabetic patients would not need to draw blood several times per day to monitor their blood glucose levels. The first step in such a study is to analyze human serum, thereby circumventing any complicating factors such as scattering from cells and absorption from hemoglobin. Berger et al. have reported such a study where six analytes in serum could be predicted with clinical or near-clinical accuracy in less than 1 min. 103 The analytes studied include glucose, cholesterol, urea, total protein, albumin, and triglycerides. They also completed a preliminary study in whole blood that was able to predict hematocrit accurately as well. These studies used the implicit method of PLS analysis. Berger et al. used the experimental setup shown in Figure 31.11B to collect Raman spectra of human serum. The experiment used 250 mw of excitation at 830 nm focused down to a 50- m diameter spot; data were collected for 5 min at 10-s intervals. A total of 69 samples was analyzed over a 7-week period, and reference concentrations were provided by standard hospital chemical analysis methods. Data were analyzed with PLS in a leave-one-week-out cross validation manner. Accurate predictions were maintained for all analytes with 60-s integration times, and some predictions could be accurately made in 10 s. Accuracy for the spectroscopic analysis is reported as root mean square error of prediction (RMSEP) compared to the reference technique. The prediction accuracies, along with the reference errors, are shown in Table As can be seen, there is good correlation for all six analytes; in fact the accuracy for some (total protein and albumin) is limited by the reference technique. As an example of the use of explicit methods, we consider the analysis of coronary artery spectra. In such a case, one collects the Raman spectra of all of the chemical or morphological components individually, keeping track of the intensity of each Raman spectrum relative to its unit concentration. The composite tissue spectrum can then simply be reconstructed by summing the basis spectra with the appropriate weighting, thereby determining the abundance of each component in the tissue (Equation 31.1). Using this approach, Brennan et al. have developed a quantitative model to characterize the relative amounts of chemical coronary artery tissue components. 116 This model consisted of basis spectra from commercially available beta carotene, cholesterol and cholesterol linoleate, chemically extracted lipids from arterial adventitia (i.e., noncholesterol lipids), isolated mineralizations dissected from highly diseased tissue (i.e., calcium hydroxyapatite), and proteins, characterized by two different spectra collected from delipidized normal and diseased arterial tissues. Appropriate calibration measurements were performed to estimate the weightings of the individual basis spectra. Tostudy the accuracy of the model, coronary arteries were obtained from explanted hearts, and therefore a wide range of disease states were represented. Samples were frozen and ground into minces, which were combined in various mixtures to vary the distribution of chemical concentrations further. It was necessary to use homogenized minces so that the Raman spectra, which were obtained from a small volume ( 1 mm 3 ), were representative of the entire sample that was subjected to chemical analysis. Raman spectra were collected from 10 different sites from each mince using the laboratory system described above with 350 mw of excitation at 830 nm over a 100- m diameter spot. The samples were then analyzed chemically to determine the amount of triglycerides, free cholesterol, cholesterol esters, phospholipids, and inorganic phosphorus (to characterize the calcium salts). Figure shows examples

23 15000 A B Intensity (a.u.) JJ JJJJJ J J J J J JJ J J J J JJJJJ JJ J J J Raman shift (cm -1 ) Raman shift (cm -1 ) Intensity (a.u.) C J JJJ J JJJ JJ J JJJ J JJJ J JJ J J JJJ J JJJ JJ Raman shift (cm -1 ) FIGURE Comparison between the model (line) and spectra (dots) of coronary artery that exhibited (A) intimal fibroplasia, (B) noncalcified atheromatous plaque, and (C) calcified plaque. The differences between a spectrum and its model fit are displayed below each comparison (same scale). (From Brennan, J.F. et al., Circulation, 96, 99, With permission.) of the model fits (line) to the data from macroscopic samples (dots) for three classes of artery. The small residuals, whose size is on the order of the noise, show that the model is relatively complete. Despite all of the assumptions used in generating the basis spectra and calibrating the model, macroscopic spectra can be accurately reconstructed, and quantitative spectroscopic analysis correlates well with the chemical assay. Table 31.2 shows correlation coefficients between the chemical assay and the Raman analysis, the bias (mean difference between the two methods), and the standard deviation ( ) of the difference between the measurements. All correlation coefficients are >0.9, with the exception of free cholesterol, which is slightly lower. The two examples discussed here show the potential for Raman spectroscopy not only for identifying the chemicals present in a sample, but also as a means for providing an optical quantitative assay. In addition to providing chemical information, it is also possible to take this information and develop diagnostic algorithms to determine disease states. 117 Also, utilizing this chemical information opens up TABLE 31.2 Prediction Accuracy of Raman Artery Chemical Model Compared to Reference Chemical Assay Component r Bias, %, % Total proteins Fats (noncholesterol) Total cholesterol Free cholesterol Cholesterol esters Calcium salts

24 the potential for studying disease progression, regression, and etiology in vivo. Such an approach may lead to advances in future therapies Enhanced Tissue Characterization via Combined Use of Spectroscopic Techniques The spectroscopic techniques described in the previous sections provide complementary information on different aspects of tissue composition, morphology, and biochemistry. Diagnostic algorithms that separate normal from diseased tissues based on specific quantitative changes that take place during disease development can be established based on each technique individually. However, medical decisions and diagnoses are generally not made on the basis of one measure. Experience has shown that the combined use of several measures provides more robust and reliable results. In the same way, it is reasonable to expect that by combining information from different spectroscopic techniques we can enhance our capability to classify tissue and our understanding of the processes that lead to the creation of a lesion. Indeed, this has been achieved in the following examples Tri-Modal Spectroscopy for Characterization and Detection of Precancerous Lesions in Vivo Tri-modal spectroscopy, or TMS, refers to the combined use of intrinsic fluorescence, diffuse reflectance and light scattering spectroscopy as a quantitative tool for characterizing tissue morphology and biochemistry. The fluorescence and reflectance spectra required for the implementation of each one of these techniques are acquired simultaneously with the FastEEM instrument shown in Figure Thus, the information extracted from IFS, DRS, and LSS characterizes the same tissue site. In this small data set, this information can be combined simply by assigning a classification to a particular tissue site consistent with the diagnostic algorithms developed for at least two of the three techniques. As a result, a significant enhancement is observed in the sensitivity and specificity with which we can separate dysplastic (lowand high-grade) lesions from nondysplastic Barrett s esophagus or high-grade from nonhigh-grade (lowgrade and nondysplastic) Barrett s esophagus. Results are presented for spectra acquired from 16 patients, 26 nondysplastic, 7 low-grade, and 7 high-grade dysplastic tissue sites (Table 31.3). 36 A similar enhancement was observed when spectroscopic information from all three techniques was combined to separate colposcopically abnormal but histopathologically benign cervical tissues from histopathologically abnormal cervical lesions. The reported sensitivities and specificities were based on logistic regression and cross validation performed on a set of spectra from 44 patients including 50 colposcopically normal, 21 colposcopically abnormal but histopathologically benign and 13 colposcopically abnormal sites classified as low-grade (2) or high-grade (11) SILs (Table 31.4). 8 A similar enhancement is also observed for detection of oral cavity lesions. 35 Ultimately, it is expected that dedicated TMS algorithms will be developed based on the weighted contributions of the individual pieces of information extracted from each technique. TABLE 31.3 Accuracy of Spectroscopic Classification of Nondysplastic (NDB), Low- Grade (LGD) and High-Grade Dysplastic (HGD) Tissue in Barrett s Esophagus HGD vs. (LGD and NDB) (LGD and HGD) vs. NDB Sensitivity Specificity Sensitivity Specificity Intrinsic fluorescence (IF) 100% 97% 79% 88% Diffuse reflectance (DR) 86% 100% 79% 88% Light scattering (LS) 100% 91% 93% 96% Combination of IF, DR, and LS 100% 100% 93% 100%

25 TABLE 31.4 Performance of Different Spectroscopic Techniques for Separating SILs from Non-SILs Biopsied Non-SILs a vs. SILs Non-SILs b vs. SILs Sensitivity Specificity Sensitivity Specificity IFS 62% 67% 62% 92% DRS 69% 57% 62% 82% LSS 77% 71% 77% 83% TMS 92% 71% 92% 90% a Biopsied non-sils include 21 colposcopically abnormal biopsied sites classified as mature squamous epithelium (5/21) or squamous metaplasia (16/21). b Non-SILs in this case include 50 colposcopically normal sites and 21 biopsied sites classified as squamous metaplasia or mature squamous epithelium. TABLE 31.5 Sensitivity, Specificity, and Positive Predictive Value of Three Different Diagnostic Algorithms for Coronary Atherosclerosis Sensitivity Specificity PPV* ÌFS 90% 82% 95% DRS 72% 68% 90% BMS* 95% 91% 98% * PPV = positive predictive value; BMS = Bi-modal spectroscopy Bi-Modal Spectroscopy for Characterizing Atherosclerotic ex Vivo Lesions Analysis of intrinsic fluorescence and diffuse reflectance spectra acquired using a FastEEM instrument from normal and diseased coronary artery ex vivo tissues indicates that significant changes can be detected in tissue composition during the development of atherosclerotic lesions. For example, analysis of intrinsic fluorescence spectra at several excitation wavelengths yields the fluorescence contributions of collagen/ elastin and ceroid. On the other hand, diffuse reflectance spectra convey important information about the levels of beta-carotene absorption. The combination of information concerning these important tissue constituents results in a significant enhancement in our ability to classify tissue spectroscopically in accordance with pathology (Table 31.5) Fluorescence and Raman Spectroscopy for Characterizing Microscopic Ceroid Deposits in Coronary Artery Samples Ceroid is postulated to be a complex of protein associated with oxidized lipids and may be responsible for causing irreversibility of certain atherosclerotic plaques. 118,119 In small uncomplicated plaques, ceroid initially appears within the cytoplasm of superficial macrophage-like foam cells. It has therefore been suggested that macrophages might be responsible for lipid oxidation in the plaque, with potentially damaging consequences, such as cell injury and necrosis and the release of insoluble material into the extracellular space, ultimately causing the plaque to become irreversible. In advanced plaques, most of the ceroid is extracellular. Ceroid is histochemically identified by insolubility in a variety of lipid solvents and the uptake of lipid dyes. In addition, ceroid is characterized by the emission of intense yellow autofluorescence when it is excited with ultraviolet (UV) light. 53,120 However, the exact chemical composition of ceroid deposits in atherosclerotic plaques is not well defined, and may vary in different samples or tissues. Insight into the

26 FIGURE (A) Fluorescence map showing the distribution of ceroid at 565-nm excitation. (B) Fluorescence spectra of collagen/elastin and ceroid acquired at 476-nm excitation. chemical composition of ceroid may provide a better understanding of the mechanism of its formation and suggest avenues to induce plaque regression with medical therapy. By spatially monitoring tissue autofluorescence, the location of ceroid within an unstained thin tissue section can be identified spectroscopically; subsequently, Raman measurements can be acquired to ascertain more detailed information on the chemical composition of the deposit. Figure 31.13A shows a fluorescence map, 477-nm excitation, that pinpoints the location of several ceroid deposits in a sample of coronary artery. Although collagen and elastin also exhibit autofluoresce in the UV, it is typically less intense than the emission from ceroid and peaks around 530 nm. This is blue shifted compared to the ceroid fluorescence maximum that occurs between 550 to 580 nm. The normalized fluorescence spectra of ceroid and collagen are displayed in Figure 31.13B. The fluorescence map in Figure 31.13A was generated by plotting the fluorescence peak intensity at 565 nm for each pixel and thus bright regions are indicative of the presence of ceroid. Following identification of ceroid deposits through their characteristic fluorescence emission, Raman spectroscopy was performed on the same region of tissue in order to elicit the chemical composition of the deposits. Due to its narrow and distinct spectral features, Raman spectroscopy is ideally suited to the study of complex chemical mixtures such as those occurring in the core of unstable atherosclerotic plaques. Figure 31.14A depicts a Raman map showing the distribution of cholesterol in the same ceroid deposit displayed in Figure 31.13A. In addition to cholesterol, several other chemical moieties, such as cholesterol esters, apoproteins, and triolene, have been identified in the deposits. After acquisition of the Raman data, the identical tissue section was lipid extracted and subsequently stained with oil red O as further confirmation of the identity of the deposit. A white light image of this stained tissue section is presented in Figure 31.14B. In this image, the deposit appears red, indicating that the substance is histochemcially identified as ceroid. By capitalizing on the well known fluorescence spectral profile of ceroid and the detailed chemical information provided by Raman spectroscopy, we were able to gain insight into the chemical composition of ceroid deposits. The combination of Raman and fluorescence spectroscopies provides a means to identify ceroid deposits and to probe their chemical composition. This might shed light on the process of lipoprotein degradation and deposition in atherosclerotic plaques Conclusion In conclusion, the preceding examples illustrate that spectroscopy reveals biochemical and morphological information about the native tissue state, without the need for tissue removal. The extracted morphological information can be similar in nature to that obtained by histopathology, as in the characterization of cell nuclear morphology using LSS. In addition, spectroscopic techniques (such as intrinsic fluorescence

27 FIGURE (A) Raman map and (B) phase contrast image of a ceroid deposit in a sample of human coronary artery. and Raman) also provide information about tissue biochemistry which cannot be directly obtained otherwise, because tissue removal and processing necessarily alter its biochemistry. Methods such as angular LSS can be sensitive to morphological structures and structural changes that cannot be resolved using even invasive conventional microscopic techniques. Furthermore, analytical models can be used to describe measured tissue spectra and extract information in a quantitative manner. Based on all of these desirable features, it is evident that spectroscopy can be a powerful tool for understanding better some of the fundamental processes that take place very early in the development of disease. The combination of information extracted from complementary spectroscopic techniques offers the opportunity to acquire a more accurate picture of the tissue state, which in turn enables more robust diagnosis of disease. The capability to perform measurements over small and wide tissue areas enhances the clinical applicability of these techniques. Based on the work already performed in this field, it is clear that spectroscopy is on the way to serving as an important adjunct to clinical and histopathological evaluation of disease. Acknowledgments The authors acknowledge contributions from Andrew Berger (now at the University of Rochester), Martin Hunter, Irving Itzkan, Maxim Kalashnikov, Tae-Woong Koo, Lev Perelman (now at Harvard Medical School), and Qingguo Zhang from the MIT Spectroscopy Laboratory; Jacques Van Dam (now at the Stanford University School of Medicine), Michael Wallace (now at the Medical University of South Carolina), Brian Jacobson, David Carr-Locke, Ellen Sheets, and Christopher Crum from the Brigham and Women s Hospital; Stanley Shapsay, Tulio Valdez, Cesar Fuentes, and Sandro Kabani from the Boston University Medical Center; John Kramer, Joseph Crowe, and Joseph Arendt from the Cleveland Clinic Foundation; Maryann Fitzmaurice from the Cleveland Clinic Foundation; and Gary Horowitz from Beth Israel/Deaconness Medical Center. The work presented in this chapter that involved collaborative projects with the MIT Spectroscopy Laboratory was supported by the National Institutes of Health grants P41RR02594, CA53717, and CA72517, and by generous support from the Bayer Corporation. References 1. Ramanujam, N., Fluorescence spectroscopy of neoplastic and non-neoplastic tissues, Neoplasia, 2, 89, Wagnières, G.M., Star, W.M. and Wilson, B.C., In vivo fluorescence spectroscopy and imaging for oncological applications, Photochem. Photobiol., 68, 603, Panjehpour, M., Overholt, B., Vo-Dinh, T., Haggit, R., Edwards, D., and Buckley, F., Endoscopic fluorescence detection of high-grade dysplasia in Barrett s esophagus, Gastroenterology, 111, 93, 1996.