Autofluorescence and diffuse reflectance spectroscopy for oral oncology

BACKGROUND AND OBJECTIVES: Autofluorescence and diffuse reflectance spectroscopy have been used separately and combined for tissue diagnostics. Previously, we assessed the value of autofluorescence spectroscopy for the classification of oral (pre-)malignancies. In the present study, we want to determine the contributions of diffuse reflectance and autofluorescence spectroscopy to diagnostic performance. STUDY DESIGN/MATERIALS AND METHODS: Autofluorescence and diffuse reflectance spectra were recorded from 172 oral lesions and 70 healthy volunteers. Autofluorescence spectra were corrected in first order for blood absorption effects using diffuse reflectance spectra. Principal Components Analysis (PCA) with various classifiers was applied to distinguish (1) cancer and (2) all lesions from healthy oral mucosa, and (3) dysplastic and malignant lesions from benign lesions. Autofluorescence and diffuse reflectance spectra were evaluated separately and combined. RESULTS: The classification of cancer versus healthy mucosa gave excellent results for diffuse reflectance as well as corrected autofluorescence (Receiver Operator Characteristic (ROC) areas up to 0.98). For both autofluorescence and diffuse reflectance spectra, the classification of lesions versus healthy mucosa was successful (ROC areas up to 0.90). However, the classification of benign and (pre-)malignant lesions was not successful for raw or corrected autofluorescence spectra (ROC areas <0.70). For diffuse reflectance spectra, the results were slightly better (ROC areas up to 0.77). CONCLUSIONS: The results for plain and corrected autofluorescence as well as diffuse reflectance spectra were similar. The relevant information for distinguishing lesions from healthy oral mucosa is probably sufficiently contained in blood absorption and scattering information, as well as in corrected autofluorescence. However, neither type of information is capable of distinguishing benign from dysplastic and malignant lesions. Combining autofluorescence and reflectance only slightly improved the results.     

In vivo autofluorescence spectroscopy of oral premalignant and malignant lesions: distortion of fluorescence intensity by submucous fibrosis

BACKGROUND AND OBJECTIVES: To test whether autofluorescence spectroscopy can be used for the diagnosis of oral neoplasia in a high-risk population, we characterized the in vivo autofluorescence spectra from oral submucous fibrosis (OSF) lesions and oral premalignant and malignant lesions in both OSF and non-OSF patients. STUDY DESIGN/MATERIALS AND METHODS: Autofluorescence emission spectra were measured under the excitation wavelength of 330 nm, using a Xenon lamp-based fluorospectrometer coupled to a handheld optical fiber probe. Autofluorescence spectroscopies were analyzed among patients with OSF lesions, and oral lesions of epithelial hyperkeratosis (EH), epithelial dysplasia (ED), and squamous cell carcinomas (SCC) and normal oral mucosa (NOM) of healthy volunteers. RESULTS: We found that the most intensely autofluorescence emission peaks occurred at 380 nm and 460 nm. For comparing the spectral patterns among different groups of oral lesions and NOM, ratios of the area under the spectrum of 460+/-10 nm to that under the spectrum of 380+/-10 nm (denoted as A(460+/-10nm)/A(380+/-10nm)) were calculated. The mean ratio values increased gradually from OSF to NOM, to EH and ED, and to SCC. The ANOVA test showed significant differences in the ratio value among all categories of samples (P<0.01). On the other hand, we found that EH, ED, and SCC lesions on OSF patients had distorted autofluorescence intensity. The mean ratio values of EH, ED, and SCC between non-OSF and OSF patients show significant differences. Furthermore, an ANOVA test showed NOM is not distinguishable from EH and ED lesions on oral fibrotic mucosa (P>0.05). CONCLUSIONS: Autofluorescence spectroscopy can be used to diagnose EH, ED, and SCC lesions in non-OSF patients but not in OSF patients.     

Autofluorescence imaging and spectroscopy of normal and malignant mucosa in patients with head and neck cancer.

BACKGROUND AND OBJECTIVE: An early detection of oral cancer might improve the patient's prognosis. We present preliminary results of autofluorescence photodetection of cancerous oral mucosa. MATERIALS AND METHODS: 49 patients were investigated altogether. In 30 patients, malignant and healthy oral mucosa were excited with violet light (lambda = 375 to 440 nm). Images were recorded by a sensitive CCD camera. Spectrophotometric analysis in the green spectral range was performed on tumorous and innocuous mucosa in 36 patients. RESULTS: In 13 patients (43.3%), tumors were subjectively better distinguishable from their surroundings through a reduction of green autofluorescence than by ordinary inspection. Tumor detection abilities varied for different locations and tumor morphologies. Spectral analysis showed contrasts in autofluorescence intensities between tumor and normal tissues in 34 patients (94.4%). Autofluorescence spectra of normal mucosa varied both inter- and intraindividually. CONCLUSIONS: Using violet excitation light, camera-based autofluorescence photodetection in the green spectral range presented a highly promising tool for the diagnosis of oral malignomas in almost half of the cases examined. The possible ways on how the obtained results could serve to find a more advanced method for a precise tumor detection in the oral cavity are being discussed.     

Autofluorescence properties of rat cerebellum cortex during postnatal development

BACKGROUND AND OBJECTIVES: The multilayered structure of rat neocerebellum cortex (VI-VIII lobules of the vermis) during postnatal development undergoes rearrangements, which in turn are affected by treatment with the anti-tumoral drug cisplatin. The dependence of autofluorescence emission properties on the tissue structural and molecular features has been investigated. STUDY DESIGN/MATERIALS AND METHODS: Autofluorescence analysis was performed at defined time points of cerebellar histogenesis--11, 17, and 30 postnatal days- under normal conditions or after 5 microg/g body weight cisplatin treatment at 10 postnatal day. Autofluorescence signal was analyzed in vivo at the surface of intact lobules of cerebellum vermis by means of fiber optic spectrofluorometry, or on tissue sections by means of microspectrofluorometry and fluorescence imaging. RESULTS: In vivo spectroscopy showed changes of autofluorescence signal both during normal histogenesis and after cisplatin treatment. External granular layer (EGL) and molecular layer (ML), that is, the more external layers were found to be interested by structural alterations, and showed the greatest changes in signal amplitude, accounting for the in vivo results. Fitting analysis indicated that changes in spectral shape reflected an increase in oxidative damages induced by cisplatin treatment. CONCLUSIONS: The results confirm the relationship of the autofluorescence emission properties with histological and biochemical features of biological tissue.     

Characterization of pediatric Wilms' tumor using Raman and fluorescence spectroscopies

Purpose

Raman spectroscopy has been successfully demonstrated as an effective tool for tissue characterization and diagnosis, but nearly all studies have interrogated adult tissues and diseases. In this study, we demonstrate the application of Raman spectroscopy and its background autofluorescence for pediatric Wilms' tumor diagnosis.

Methods

Eight tumors were measured in this study, along with matched normal kidney tissue in 6 cases. Spectral comparisons were drawn, and diagnostic use was assessed using both the Raman spectral features as well as the inherent tissue fluorescence.

Results

The fluorescent background spectra were able to discriminate normal kidney from Wilms' tumor with 81% sensitivity and 100% specificity. The Raman spectra obtained 93% sensitivity and 100% specificity.

Conclusions

This pilot study shows that both autofluorescence and Raman spectra provide diagnostic use in discriminating Wilms' tumor from normal kidney. These techniques may be used individually or in tandem to develop a real-time intraoperative screening and diagnostic device.     

Autofluorescence characteristics of healthy oral mucosa at different anatomical sites

BACKGROUND AND OBJECTIVES: Autofluorescence spectroscopy is a promising tool for oral cancer detection. Its reliability might be improved by using a reference database of spectra from healthy mucosa. We investigated the influence of anatomical location on healthy mucosa autofluorescence. STUDY DESIGN/MATERIALS AND METHODS: Spectra were recorded from 97 volunteers using seven excitation wavelengths (350-450 nm), 455-867 nm emission. We studied intensity and applied principal component analysis (PCA) with classification algorithms. Class overlap estimates were calculated. RESULTS: We observed differences in fluorescence intensity between locations. These were significant but small compared to standard deviations (SD). Normalized spectra looked similar for locations, except for the dorsal side of the tongue (DST) and the vermilion border (VB). Porphyrin-like fluorescence was observed frequently, especially at DST. PCA and classification confirmed VB and DST to be spectrally distinct. The remaining locations showed large class overlaps. CONCLUSIONS: No relevant systematic spectral differences have been observed between most locations, allowing the use of one large reference database. For DST and VB separate databases are required.     

Effect of Fixation and Embedding on Raman Spectroscopic Analysis of Bone Tissue

Raman spectroscopy provides valuable information on the physicochemical properties of hard tissues. While the technique can analyze tissues in their native state, analysis of fixed, embedded, and sectioned specimens may be necessary on certain occasions. The information on the effects of fixatives and embedding media on Raman spectral properties is limited. We examined the effect of ethanol and glycerol as fixatives and a variety of embedding media (Araldite, Eponate, Technovit, glycol methacrylate, polymethyl methacrylate, and LR white) on Raman spectral properties (mineralization, crystallinity, and carbonation) measured from the cortical bone of mouse humeri. Humeri were fixed in ethanol or glycerol, followed by embedding in one of the media. Nonfixed, freeze-dried, and fixed but not embedded sections were also examined. Periosteal, endosteal, and midosteal regions of the intracortical envelope were analyzed. Raman spectra of fixative solutions and embedding media were also recorded separately in order to examine the specifics of overlap between spectra. We found significant effects of fixation, embedding, and anatomical location on Raman spectral properties. The interference of ethanol with tissue seemed to be relatively less pronounced than that of glycerol. However, there was no single combination of fixation and embedding that left Raman spectral parameters unaltered. We conclude that careful selection of a fixation and embedding combination should be made based on the parameter of interest and the type of tissue. It may be necessary to process multiple samples from the tissue, each using a combination appropriate for the Raman parameter in question.     

Relevance vector machine for optical diagnosis of cancer

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

Vision enhancement system for detection of oral cavity neoplasia based on autofluorescence

BACKGROUND: Early detection of squamous cell carcinoma (SCC) in the oral cavity can improve survival. It is often difficult to distinguish neoplastic and benign lesions with standard white light illumination. We evaluated whether a technique that capitalizes on an alternative source of contrast, tissue autofluorescence, improves visual examination. METHODS: Autofluorescence of freshly resected oral tissue was observed visually and photographed at specific excitation/emission wavelength combinations optimized for response of the human visual system and tissue fluorescence properties. Perceived tumor margins were indicated for each wavelength combination. Punch biopsies were obtained from several sites from each specimen. Sensitivity and specificity were evaluated by correlating histopathologic diagnosis with visual impression. RESULTS: Best results were achieved with illumination at 400 nm and observation at 530 nm. Here, sensitivity and specificity were 91% and 86% in discrimination of normal tissue from neoplasia. This compares favorably with white light examination, in which sensitivity and specificity were 75% and 43%. CONCLUSIONS: Oral cavity autofluorescence can be easily viewed by the human eye in real time. Visual examination of autofluorescence enhances perceived contrast between normal and neoplastic oral mucosa in fresh tissue resections.     

Clinical recording of laser-induced fluorescence spectra for evaluation of tumour demarcation feasibility in selected clinical specialities

Laser-induced autofluorescence spectra from humans were recorded in vivo at three different clinics in a study aimed at investigating the capability of this method to discriminate between malignant tumours and normal surrounding tissues. For the recordings a mobile trolley with the necessary equipment was constructed for use in an examination room or in an operating theatre environment. Laser light was guided through a 600m optical fibre to the target tissue. The fluorescence from the excited tissue was collected with the same fibre and was fed to an optical multichannel analyser. Two excitation wavelengths were used (337 and 405 nm) in order to optimize the fluorescence signals in two interesting wavelength regions (380–500 and 550–700 nm). Oral and oropharyngeal tumours excited with 405 nm light contained detectable endogenous porphyrins and were in this way discriminated from the normal mucosa. Astrocytoma grade III–IV fluorescence different from that of normal brain tissue, while tumours in the bronchial tree were not detectable using the spectral shape of the pure tissue autofluorescence.     

Time-resolved autofluorescence spectroscopy for classifying normal and premalignant oral tissues

BACKGROUND AND OBJECTIVES: Time-resolved autofluorescence spectroscopy has been used for effectively distinguishing normal tissues from precancers and cancers in various organs. The aim of this study was to find out the possibility of using time-resolved autofluorescence spectroscopy to differentiate normal oral mucosa (NOM) from oral premalignant lesions including verrucous hyperplasia (VH), epithelial hyperplasia (EH), and epithelial dysplasia (ED). STUDY DESIGN/MATERIALS AND METHODS: Time-resolved autofluorescence spectra at 633 nm under 410-nm excitation were recorded for 15 VH, 9 EH, 14 ED, and 38 NOM samples. The two-component lifetimes of the obtained curves were calculated, and a Fisher's discriminant analysis (FDA) was employed for distinguishing these tissue samples. RESULTS: After two-component lifetimes for all samples being calculated, a two-dimensional scatter plot was developed, in which 76 oral tissue samples were separated into three groups by FDA. With a leave-one-out method, the FDA algorithm gave an accuracy rate of 93% for ED, of 75% for VH and EH, and of 100% for NOM samples. In addition, all oral premalignant lesions (including VH, EH, and ED) could be distinguished from NOM samples by this FDA algorithm. CONCLUSIONS: We conclude that time-resolved autofluorescence spectroscopy at 633 nm under 410-nm excitation, based on two-component lifetime calculation and FDA, is a very sensitive technique for in vivo diagnosis of oral premalignant lesions. .     

Argon Laser Induced Autofluorescence May Distinguish Between Normal and Tumor Human Urothelial Cells: A Microspectrofluorimetric Study

Purpose

To assess the ability of argon laser-induced autofluorescence spectroscopy (LIAFS) to discriminate normal from tumor human urothelial cells.

Materials and Methods

Emission spectra of single living cells excited at 488 nm. have been studied with a confocal microspectrofluorimeter.

Results

Cellular autofluorescence appeared as a broad band with a maximum in the same “green” spectral range, 550 to 560 nm., probably corresponding to oxidized flavoprotein emission. However, the maximum autofluorescence intensity of normal urothelial cells was much higher, 10 times (p less than 0.0001) that of any of the tumor cell types tested.

Conclusion

These results, suggesting a significantly reduced oxidized flavoprotein concentration in tumor urothelial cells, should prompt us to evaluate argon LIAFS as a potential tool to detect occult urothelial severe dysplasia and carcinoma in situ.     

PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis

BACKGROUND AND OBJECTIVES: For effective management of oral neoplasia, autofluorescence spectroscopy was conducted on patients with different characteristics of oral lesions in vivo. This study tested the possibility of using a multivariate statistical algorithm to differentiate human oral premalignant and malignant lesions from benign lesions or normal oral mucosa. STUDY DESIGN/MATERIALS AND METHODS: A fiber optics-based fluorospectrometer was used to measure the autofluorescence spectra from healthy volunteers (NOM) and patients with oral lesions of submucous fibrosis (OSF), epithelial hyperkeratosis (EH), epithelial dysplasia (ED), and squamous cell carcinoma (SCC). A partial least-squares and artificial neural network (PLS-ANN) classification algorithm was used to characterize these oral lesions to discriminate premalignant (ED) and malignant (SCC) tissues from "benign" (NOM, OSF, and EH) tissues. RESULTS: The normalized and centerized spectra of the different kinds of samples showed similar but divergent patterns. Our PLS-ANN classification algorithm could differentiate "premalignant and malignant" tissues from "benign" tissues with a sensitivity of 81%, a specificity of 96%, and a positive predictive value of 88%. CONCLUSIONS: We conclude that the PLS-ANN classification algorithm based on autofluorescence spectroscopy at 330-nm excitation is useful for in vivo diagnosis of OSF as well as oral premalignant and malignant lesions.     

Label-free detection of human enteric nerve system using Raman spectroscopy: A pilot study for diagnosis of Hirschsprung disease

BackgroundHirschsprung disease (HSCR) is characterized by the absence of an enteric nerve system (ENS). To remove aganglionosis, bowel reconstruction is only a curative treatment. It is mandatory to identify the extent of aganglionosis during surgery. Raman spectroscopy is a nondestructive chemical analysis technique that provides detailed information regarding molecular vibrations. The purpose of this study is to detect the ENS using Raman spectroscopy in the human intestine for diagnosis of HSCR.MethodsThe Raman spectra of each layer of the gastrointestinal wall were collected from surgical specimens of the human rectum. Based on collected spectral data, principal component analysis was performed to determine the ENS. Subsequently, the Raman spectra of HSCR sections were analyzed.ResultsMolecular structures of the gastrointestinal wall were characterized by Raman spectroscopy. Raman spectroscopy could discriminate between ganglion and muscle layers, and the spectra of the border between muscle layers in the aganglionosis were collagen-associated peaks. Either absence on presence of ENS was also confirmed in HSCR material.ConclusionsLabel-free detection of the ENS was successfully demonstrated using Raman spectroscopy. Since this is a preliminary study, the strategy which may contribute to differentiate between ganglionic and aganglionic segments using noninvasive techniques in HSCR should be evaluated by prospective studies in near future.     

Autofluorescence spectrofluorometry of central nervous system (CNS) neuromediators

BACKGROUND AND OBJECTIVES: Changes in the neurotransmitter 5-hydroxytriptamine (5-HT) are related to psychiatric diseases such as depression and anxiety. In this study, 5-HT autofluorescence properties were investigated in solution and in biological tissues. STUDY DESIGN/MATERIALS AND METHODS: Spectrofluorometric characterization was performed on ex vivo samples (tissue sections, homogenates) of the 5-HT-rich brain region hippocampus from rats untreated or treated to affect endogenous 5-HT levels; in vivo, with a 50 solidus in circle optic fiber probe positioned via stereotaxis. RESULTS: 5-HT exhibited minor excitation and emission bands at wavelengths longer than the well known excitation and emission bands in the UV region, 250-320 nm. Spectrofluorometric measurements under 366 nm excitation on homogenates supplied with 5-HT or belonging to treated rats revealed spectral alterations attributable to changes in the amount of 5-HT. Ex vivo and in vivo autofluorescence data were consistent with those obtained by conventional voltammetry. CONCLUSIONS: Autofluorescence spectroscopy potential is confirmed as a suitable technique for the direct measurement of neurotransmitters.     

贲门癌与同体正常胃组织的显微自体荧光对比研究

目的探讨贲门癌组织显微自体荧光特征及自体荧光在同体正常胃组织各层的分布和差异。方法采用激光扫描共聚焦显微镜以氩离子(Ar+)激光(Ex=488nm)和氦氖激光(He-Ne)(Ex=543nm)为激发光的双通道法对16例贲门癌手术标本(贲门癌组织与同体胃体组织)进行自体荧光图像分析。结果同体正常胃组织胃壁各层中,以黏膜下层荧光信号最强。固有层呈现较强的荧光信号,主要分布于腺体上。癌组织自体荧光信号与正常组织各层相比均显著减弱(P<0.01)。结论贲门癌组织的显微自体荧光与同体正常胃组织在形态、颜色、分布及荧光强度上都存在明显差异。     

Differentiation of small round blue cell tumors using Raman spectroscopy

Purpose

Small round blue cell tumors (SRBCTs) are aggressive undifferentiated embryonal tumors, including neuroblastoma, rhabdomyosarcoma, Ewing sarcoma, and non-Hodgkin lymphoma. They share similar histologic features. Additional studies such as immunohistochemistry and molecular techniques are required to differentiate them. There is no widely available tool for real-time diagnosis. Raman spectroscopy is an analytical technique with potential for quick and accurate diagnosis of tumors in near real-time.

Methods

Fresh or banked frozen tissue samples from SRBCTs were processed for routine pathology and Raman spectroscopy. Raman results were correlated with the final pathology diagnosis.

Results

The data set was composed of 480 spectra from 32 samples, including 179 neuroblastoma, 37 Ewing sarcoma, 164 rhabdomyosarcoma, and 100 non-Hodgkin lymphoma spectra.Discriminant function analysis showed that a combination of 18 peaks could accurately identify 94% of spectra. At the tissue level, all tumors were correctly identified.Only 10 peaks were needed to classify all tissues with 100% accuracy. Spectral-level classification with this model was 87.9%.

Conclusion

Raman spectroscopy is an accurate technique for quickly and accurately differentiating SRBCTs. It could diagnose these specimens and provide a single, easy to use test for near real-time diagnosis.     

Raman spectroscopy for neoplastic tissue differentiation: a pilot study

Background

Several changes occur during the transformation of normal tissue to neoplastic tissue. Such changes in molecular composition can be detected by Raman spectroscopy. Raman spectroscopy is a nondestructive method of measuring these changes, which suggests the possibility of real-time diagnosis during medical procedures.

Methods

This study seeks to evaluate the ability of Raman spectra to distinguish tissues. The Raman signatures of normal kidney, lung, and liver tissue samples from pigs and rats were characterized in vitro. Further, a human neuroblastoma and a hepatoblastoma, obtained at resection were also studied.

Results

The Raman spectra of the animal samples of kidney, liver, and lung are distinctly different in the intensity distribution of the Raman peaks. Further, the spectra of a given organ from pigs and rats, although similar, were different enough to distinguish between the 2 animals. In the patient tissues, the Raman spectra of normal liver, viable tumor, and fibrotic hepatoblastoma were very different. Fibrotic tissue showed a greater concentration of carotenoids, whereas viable tissue was rich in proteins and nucleic acids. The normal tissue showed both components. Similar differences were also seen in the neuroblastoma tissue.

Conclusions

The results of this study show the potential use of Raman spectroscopy in clinical diagnosis.     

Diagnosis of Wilms' tumor using near-infrared Raman spectroscopy

Purpose

Raman spectroscopy has distinguished malignant from normal tissues in several types of cancer. This is the first report of applying Raman spectroscopy to the diagnosis of Wilms' tumor.

Methods

Specimens of normal kidney, Wilms' tumor, xanthogranuloma, nephrogenic rests, and rhabdoid tumor were collected fresh from the operating room. Specimens of Wilms' tumor, normal kidney, and congenital mesoblastic nephroma were retrieved from the cryobank and thawed to room temperature. At least 12 Raman spectra were collected from each tissue sample. Histologic slides of each specimen were reviewed by pediatric pathologists. A computer algorithm based on discriminant function analysis (DFA) classified the Raman spectra of Wilms' tumor and the normal sample.

Results

Four hundred sixty-seven spectra were collected from 41 specimens. Using DFA, Raman spectroscopy differentiated Wilms' tumor from normal with 100% sensitivity and specificity and treated from untreated Wilms' tumor with 100% sensitivity and specificity. Using a DFA model built from cryopreserved specimens but applied to fresh Wilms' and normal samples, the sensitivity and specificity were 93.3% and 90.9%, respectively.

Conclusion

Raman spectroscopy is an accurate technique for differentiating Wilms' tumor from normal kidney and treated from untreated Wilms' tumor. It has potential to diagnose in minutes what currently takes several hours to days.     

Multivariate analysis of laryngeal fluorescence spectra recorded in vivo

BACKGROUND AND OBJECTIVE: The potential of using various multivariate analysis methods for classification of fluorescence spectra acquired in vivo from laryngeal tissues in Patients was investigated. STUDY DESIGN/MATERIALS AND METHODS: Autofluorescence spectra were measured on 29 normal tissue sites and 25 laryngeal lesions using 337-nm excitation. Four different multivariate analysis schemes were applied. Laryngeal fluorescence spectra from patients who had been administered delta-aminolevulinic acid (ALA) were obtained using 405-nm excitation and were classified using partial least squares discriminant analysis (PLS-DA). RESULTS: For autofluorescence spectra, logistic regression based on principal component analysis (PCA) or PLS, or PLS-DA all resulted in sensitivities and specificities around 90% for lesion vs. normal. Using ALA and 405-nm excitation gave a sensitivity of 100% and a specificity of 69%. CONCLUSION: Multivariate analysis of fluorescence spectra could allow classification of laryngeal lesions in vivo with high sensitivity and specificity. PLS performs at least as well as PCA, and PLS-DA performs as well as logistic regression techniques on these data.