Near-infrared Raman spectroscopy for the classification of epithelial pre-cancers and cancers

JOURNAL OF RAMAN SPECTROSCOPYJ. Raman Spectrosc. 2002; 33: 564–573Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/jrs.882

Near-infrared Raman spectroscopy for theclassification of epithelial pre-cancers and cancers

Nicholas Stone,1∗ Catherine Kendall,1 Neil Shepherd,2 Paul Crow1,3 and Hugh Barr3

1 Optical Diagnostics Group, Cranfield Postgraduate Medical School, Gloucestershire Royal Hospital, Great Western Road, Gloucester GL1 3NN, UK2 Department of Cellular Pathology, Gloucestershire Royal Hospital, Great Western Road, Gloucester GL1 3NN, UK3 Department of Surgery, Gloucestershire Royal Hospital, Great Western Road, Gloucester GL1 3NN, UK

Received 30 October 2001; Accepted 13 March 2002

The use of near-infrared Raman spectroscopy to interrogate epithelial tissue biochemistry and hencedistinguish between normal and abnormal tissues was investigated. Six different epithelial tissues fromthe larynx, tonsil, oesophagus, stomach, bladder and prostate were measured. Spectral diagnostic modelswere constructed using multivariate statistical analysis of the spectra to classify samples of epithelialcancers and pre-cancers. Tissues were selected for clinical significance and to include those which developinto carcinoma from squamous, transitional or columnar epithelial cells. Rigorous histopathologicalprotocols were followed and mixed pathology tissue samples were discarded from the study. Principalcomponent fed linear discriminant models demonstrated excellent group separation, when tested by cross-validation. Larynx samples, with squamous epithelial tissue, were separated into three distinct groupswith sensitivities ranging from 86 to 90% and specificities from 87 to 95%. Bladder specimens, containingtransitional epithelial tissue, were separated into five distinct groups with sensitivities of between 78and 98% and specificities between 96 and 99%. Oesophagus tissue can contain both squamous andcolumnar cell carcinomas. A three group model discriminated the columnar cell pathological groups withsensitivities of 84–97% and specificities of 93–99%, and an eight group model combining both columnarand squamous tissues in the oesophagus was able to discriminate pathologies with sensitivities of 73–100%and specificities of 92–100%. It is likely that any overlap between pathology group predictions will havebeen due to a combination of the difficulty in histologically distinguishing between pre-cancerous statesand the fact that there is no biochemical boundary from one pathological group to the next, i.e. there isbelieved to be a continuum of progression from the normal to the diseased state. Copyright 2002 JohnWiley & Sons, Ltd.

INTRODUCTION

The primary requirement for successful treatment of cancer isearly detection. Current methods of detecting malignanciesrely upon surveillance of at risk populations or on diag-nostic investigations following presentation with suspicioussymptoms. By the time symptoms are present, tumours areusually of a significant size, and it is often too late to facilitatea full cure. The majority of malignancies originate in epithe-lial tissue, usually found lining the surfaces of organs. Theseepithelial cancers are called carcinomas. They are especiallyof interest because they develop over relatively long time-scales on the surface or lining of an organ prior to invasioninto deeper tissues. Endoscopic access can often be achieved,

ŁCorrespondence to: Nicholas Stone, Optical Diagnostics Group,Cranfield Postgraduate Medical School, Gloucestershire RoyalHospital, Great Western Road, Gloucester GL1 3NN, UK.E-mail: [email protected]/grant sponsor: MedLINK UK.

thus making the surveillance or screening of at-risk popu-lations possible. Furthermore, the process of carcinogenesisin many epithelial tissues, although not fully understood, isknown to frequently include a pre-malignant state. If thismicroscopic cellular change can be detected then early treat-ments, likely to be less damaging and more successful, canbe performed.

Cancers of the larynx, oesophagus and bladder accountfor 9% of all cancers in adults in England and Wales, withapproximately 2000, 5000 and 11 000 new cases per year forlarynx, oesophagus and bladder malignancies, respectively.1

The incidence of all three cancers has risen over the pastdecades,1,2 although the rate of incidence of oesophagealadenocarcinomas occurring in Barrett’s oesophagus has risenmore rapidly than for any other cancer.3,4 The transformationto carcinoma in all three organs involves an intermediate orpre-malignant step, whereby the cells in the epithelial layerbegin to grow and mature abnormally. This state is calleddysplasia, although each organ exhibits visually distinctdysplastic cells depending on the original epithelial cells

Copyright 2002 John Wiley & Sons, Ltd.

Classification of epithelial pre-cancers and cancers 565

present. For example, long periods of exposure to gastro-oesophageal reflux can lead to a change in the lining of theoesophagus, whereby the normal squamous epithelial liningis replaced by protective columnar epithelial cells. This isoften termed Barrett’s oesophagus and it is associated with anincreased risk of developing oesophageal adenocarcinoma.5,6

Barrett’s dysplasia is the most frequently used marker ofincreased cancer risk in Barrett’s oesophagus. It is morecommonly seen in areas of intestinal metaplasia (IM), oneof three types of Barrett’s oesophagus, and it is unlikelythat oesophageal adenocarcinoma occurs except in patientswith IM.7,8 The potential benefits of removing an oesophagusexhibiting dysplasia must be weighed against the relativelyhigh mortality associated with oesophagectomy (5–15%)9

and the poor outcome in patients who present with invasiveadenocarcinoma of the oesophagus (23% survival at 1 yearand 7% survival at 5 years in England and Wales between1986 and 1990).1 Although survival rates for laryngeal andbladder cancers are better (55–65% at 5 years),1 it is delayeddiagnosis leading to systemic metastatic invasion that is themain reason for poor survival from epithelial cancers.10

The ‘gold standard’ for the detection of malignan-cies and pre-malignancies is excisional biopsy followed byhistopathological analysis. This technique relies upon sec-tioning tissue less than one cell thick (<10 µm), staining withhaematoxylin and eosin (H&E) and viewing under a con-ventional light microscope. The analysis depends mainly onthe subjective recognition of tissue morphology and architec-tural patterns. There are significant difficulties in obtainingan accurate diagnosis using the ‘gold standard.’ First, theselection of biopsies for the detection of invisible microscopicsurface lesions must rely upon a blind targeting protocol. Thiscan lead to a high probability of missing abnormal tissue andlarge numbers of normal samples will be generated. A studyin the oesophagus following 16 patients undergoing surveil-lance for Barrett’s oesophagus demonstrated that even arigorous sampling protocol will be likely to miss abnormallesions previously found in the organ.11 Second, in the anal-ysis of pre-cancerous lesions there are often high levels ofdiscrepancy between pathologists, owing to the subjectivenature of histopathological analysis. Studies grading dys-plasia in ulcerative colitis and colonic adenomas producedan overall agreement between any two pathologists rangingfrom 42 to 65%.12,13 Similar studies demonstrated agreementof between 58 and 61% for oesophageal dysplasia14 and 54%for laryngeal dysplasia.15 Furthermore, the removal of tis-sue samples can have a damaging effect on the function ofthe organ, especially in sensitive organs such as the larynx,where permanent loss of voice may result.

Histopathological examination of biopsy samples reliesupon the subjective assessment of tissue architecture, whichis likely to exhibit abnormal changes at a later stage than sub-cellular biochemical changes. Evidently, the developmentof a technique, permitting the objective, non-invasivequalitative biochemical analysis of tissue would be of great

value. The potential of Raman spectroscopy to distinguishbetween normal and malignant tissue has been demonstratedby a number of workers.16 – 20

The objective of this study was to evaluate the potentialfor near-infrared (NIR) Raman spectroscopy, a highly specificoptical analysis technique, to classify or discriminate betweenthe pathological state of different epithelial tissues and thusdemonstrate a future capability for in vitro classificationand in vivo detection of early epithelial malignancies. Thelarynx, oesophagus and bladder are the main subjects ofthis work. Carcinomas in each organ develop from threedistinctly different types of epithelial cells. This has allowedthe evaluation of Raman spectral analysis for detectionand classification of disease in squamous, transitional andcolumnar epithelial cells. To date only the oesophagus hasbeen examined in any detail with Raman spectroscopy,although only selected pathologies were compared.17,21

This study followed extremely rigorous sampling pro-cedures to reduce the errors caused by discrepancies inhistopathology, sample misorientation and by measuringsamples with mixed pathology. In addition, the full spec-trum of malignant disease found in each organ was studied,in order to remove any bias likely to be found in studiescomparing normal with cancerous tissue, i.e. the extremepathologies, but not the stages in between.

EXPERIMENTAL

Tissue samplesTissue specimens were collected for this study during routineendoscopic or surgical procedures. Informed written consentwas obtained from each patient and the study was approvedby the relevant Local Research Ethics Committees (UK). Thetissue samples were removed from the biopsy forceps andorientated on acetate paper to enable a depth cross-sectionthrough the tissue to be seen. The mounted sample was thenplaced in a 2 ml cryovial (BDH) and dropped into liquidnitrogen. Histological tissue cross-sections were cut fromeach of the samples using a freezing-microtome and stainedwith H&E. The use of the acetate backing paper made itpossible to adhere the sample to the microtome withoutthe use of a cutting agent and thus minimize chemicalcontamination to the tissue sample. Stained sections wereanalysed by between one and three registry histopathologistsaccording to organ-specific architectural and morphologicalgrading systems.22 Only homogeneous samples with clearlydefined pathologies were used in this investigation and thosewith mixed or indeterminate pathologies were discarded.In addition only oesophagus samples with a unanimousagreement from all three pathologists were used for thisstudy. Both of these precautions were implemented to reduceerrors from using misclassified and mixed pathology tissuesamples. The retained samples (biopsy blocks) were storedat �85 °C until spectroscopic studies were carried out. Prior

Copyright 2002 John Wiley & Sons, Ltd. J. Raman Spectrosc. 2002; 33: 564–573

566 N. Stone et al.

to performing spectral measurements, the specimens werepassively warmed to room temperature.

A total of 41 laryngeal biopsy specimens were collectedfrom 35 patients undergoing routine microlaryngoscopy.The larynx sections were classified by a histopathologist aseither normal squamous epithelium; squamous dysplasiaor squamous cell carcinoma. Twenty-five samples from 20patients were retained for analysis with Raman spectroscopy,including 14 histologically normal samples, five exhibitingdysplasia and six with squamous cell carcinoma.

Over 150 oesophageal jumbo biopsy samples were har-vested from patients undergoing endoscopic surveillancefor dysplasia in Barrett’s oesophagus. Following section-ing and histopathological analysis, only 89 homogeneoussamples from 44 patients remained. A blind consensus ofhistopathological opinion was achieved on 50 of these. Thesamples with consensus, retained for study with Ramanspectroscopy, exhibited eight different pathological states[normal squamous epithelium, cardiac Barrett’s (CB), fundicBarrett’s (FB), intestinal metaplasia (IM), high-grade dys-plasia (HGD), adenocarcinoma, squamous dysplasia andsquamous cell carcinoma (SCC)]. Of all the samples in thisstudy there was no consensus of agreement on samplesexhibiting low-grade dysplasia (LGD) and therefore thisgroup was omitted from the consensus prediction modeldescribed in this paper.

Bladder samples exhibiting normal, carcinoma in situ(CIS), low-grade carcinoma (LGC), moderate-grade carci-noma (MGC) and high-grade carcinoma (HGC) were har-vested during routine cystoscopy. Twelve homogeneoussamples from 12 patients were retained for analysis.

Other samplesHomogeneous normal samples of stomach, prostate andtonsil were also studied to demonstrate the facility ofNIR-Raman spectroscopy to provide repeatable analysis oftissue biochemistry in other epithelial tissues. Additionally,confounding factors or substances likely to contaminateepithelial tissue spectra were collected and studied. Theseincluded mucus, bile, blood, a coffee and spittle mixture andendoscope lubricant.

Raman spectroscopyRaman scattering measurements were performed in vitrowith a customized Renishaw System 1000 Raman micro-spectrometer. A Spectra-Physics argon ion (2017) pumpedTi:sapphire laser (3900S) was tuned to provide an excita-tion wavelength of 830 nm and a custom set of edge filters(Renishaw) were used to reject elastic scattered light of thelaser wavelength. A single dispersion grating was used toseparate the collected light into its spectral components:1200 lines mm�1 for laryngeal tissue measurements and300 lines mm�1 for oesophagus and bladder tissue mea-surements. To allow analysis of the full fingerprint region(400–1800 cm�1) of the tissue spectra it was necessary, with

the higher line density grating, to scan the spectrum acrossthe CCD using the ‘extended scan mode.’ The lower densitygrating allowed measurement of the full fingerprint regionof the tissue spectrum without the need to move the grating.However, a compromise in spectral resolution was made,but this has been shown not to affect the quality of the data.High-quality NIRPLAN optics were selected to minimizetheir spectral contribution in the fingerprint region, reducingthe requirement for optics signal subtraction.

Samples were orientated so that the incident laser beamilluminated the epithelial surface, with a similar geometry tothat which would be used in vivo. Tissue samples were heldin position on the microscope stage between two calciumfluoride slides. A ð 80 ultra-long working distance lenswas used to focus the laser beam (power at the sampleof 32 š 1.1 mW) to a spot size of 2–3 µm on the tissue surfaceand collect the scattered photons in non-confocal mode. Atleast five spectra were acquired from each larynx sample(mean number D 7.96, range 5–12) and a minimum of 10spectra were acquired from each oesophagus sample (meannumber D 14, range 10–20). The scattered Raman signalwas integrated for 30 s and measured over a spectral rangeof 400–1800 cm�1with respect to the excitation frequency.Bladder spectral measurements were performed in the samemanner, except that a ð20 objective was used with anillumination power of ¾85 mW. At least 15 spectra wereacquired from each sample and spectra were integrated for10 s. The changes in spectrometer configuration betweenlaryngeal to oesophageal to bladder spectral datasets werecaused by ongoing optimization processes with the aim ofmaximizing the signal and minimizing integration times.

The Raman spectrometer was calibrated for wavenumbershift using a neon lamp standard and Renishaw WiREsoftware. Tissue spectra were corrected for the energy-dependent response of the system by multiplying themeasured spectrum by the energy transfer function, whichwas measured using a tungsten-filament lamp with aspectral output calibrated by the UK National PhysicalLaboratory. No further manipulation of the tissue spectrawas performed prior to analysis. Intra-sample repeatabilitywas evaluated and the mean spectra for each tissue pathologywere obtained. Any correlation between Raman spectralvariations and histopathology was assessed using empiricaland multivariate data analysis techniques.

RESULTS AND DISCUSSION

The use of 830 nm excitation radiation facilitated theacquisition of high-quality, low-fluorescence spectra in shorttime-scales. Little background fluorescence is present in thespectra and the background slope is almost exclusivelycaused by the diminishing CCD sensitivity at longerwavelength. Signal-to-noise ratios between 10 and 20 wereachieved (depending on the signal strength of the mode)


Classification of epithelial pre-cancers and cancers

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Plate 1. Mean Raman spectra from each pathological groupin the laryngeal tissue. Spectra have been normalized to theintensity of the 1446 cm�1 peak.

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Plate 3. Mean Raman spectra from all pathologies present inthe oesophageal tissue specimens studied. Spectra have beennormalized to the intensity of the 1446 cm�1 peak.

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Plate 5. Mean Raman spectra from all bladder specimensstudied, grouped by pathology. Spectra have been normalizedto the intensity of the 1446 cm�1 peak.

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Plate 2. Plot of linear discriminant function weights for eachlarynx spectrum, when tested against the optimized model usinga cross-validation process.

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Plate 4. Plot of linear discriminant function weights of thethree-group oesophagus model for each spectrum, when testedagainst the model using a cross-validation process.

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Plate 6. Plot of linear discriminant function weights foreach spectrum, when tested against the model using across-validation process.

Copyright 2002 John Wiley & Sons, Ltd. J. Raman Spectrosc. 2002; 33

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Plate 7. Plot of linear discriminant function weights of the eight-group oesophagus model for each spectrum, when tested againstthe model using a cross-validation process.

Copyright 2002 John Wiley & Sons, Ltd. J. Raman Spectrosc. 2002; 33


in epithelial spectra when using the ð80 objective, the 300lines mm�1 grating and 30 s of signal integration.

Plots of mean, energy sensitivity corrected, Ramanspectra from the normal epithelial tissues studied (larynx,oesophagus, bladder, tonsil, stomach and prostate) areshown in Fig. 1 to enable similarities and differences tobe visualized. The Raman spectra of the normal epithelialtissues are very similar, but subtle variations exist. The mostsignificant of these can be seen in the wavenumber ranges800–980 and 1200–1400 cm�1.

The spectral repeatability over 100 spectra from 10normal oesophageal specimens from different patients wasevaluated. The spectral shape was consistent and thewavenumber positions of the peaks vary about the meanpositions by š1.12 cm�1. However, there was a significantbaseline intensity variation from one sample to the next. Themean spectrum from 1200 oesophagus spectra, covering allpathologies exhibited in the measured specimens, is shownin Fig. 2. The most significant vibrational modes have hadtheir mean wavenumber positions labelled. An attempt toassign these peaks tentatively has been made in Table 1.

LarynxLaryngeal spectra were combined into three separate groups,each defined by the tissue pathology of the measured spec-imen. The mean spectra for each group, normal squamous

mucosa, squamous dysplasia and squamous cell carci-noma, were calculated, normalized to the peak intensityat 1446 cm�1 and plotted in Plate 1. Visual inspection of thisplot shows that the differences observed in the spectra fromspecimens at different stages of cancer development are verysubtle. At first glance the spectra appear almost identical.However, a closer study of the data reveals miniscule changesin peak heights from one group to the next. Difference spectraindicate that the magnitude of the spectral variance betweenpathology groups is between 1 and 2% of the Raman spec-tral signal. Therefore, rather than empirically select peaksor peak ratios for significance of correlation between inten-sity and pathology group, all major peak intensities weremeasured for each spectrum in the data set and their ratioscalculated. These peak intensities and peak intensity ratioswere then tested for significance of group separation usinganalysis of variance (ANOVA). The inter-sample variationof individual peak intensities was large enough to obscurethe inter-group differences. However, the use of peak ratioscompensated for the overall baseline intensity changes fromone sample to the next. The most significant peak ratio (Fvalue 33.46, p value −0.0001) for separation of the threegroups was shown to be I754/I780. Mean intensity ratios of0.953 š 0.036, 0.942 š 0.035 and 0.906 š 0.041 were observedfor normal, dysplastic and cancerous tissue, respectively.Although on average this peak ratio does vary significantlywith pathology group, the variation between spectra were

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Figure 1. Mean energy sensitivity corrected Raman spectra from selected normal epithelial tissues.


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Figure 2. Mean Raman spectrum from all samples used in the oesophagus study. Wavenumber positions of major peaks arelabelled.

large enough to make this method of discrimination poor forspectral correlation with pathology.

It is likely that the extremely complex changes fromnormal to cancerous tissue will be accompanied by manymolecular variations. Therefore, spectral analysis techniquesinvolving all the data in the spectra will be more likelyto describe the subtle but complex spectral variations.Principal components (PCs) were used to describe the spectraas a linear combination of weights and loading vectors.Each loading vector or principal component described everdecreasing variances in the spectra from the mean of thedataset. PCA was performed to reduce the number ofvariables in the analysis. The weights of the principalcomponents were used as the variables for entry into alinear discriminant analysis (LDA) model, which maximizedthe variance between groups and minimized the variancewithin groups. The multivariate analysis routines wereperformed using the PLS-toolbox (Eigenvector) on theMATLAB platform. Spectral datasets were pre-processedusing Savitsky–Golay filters and mean-centering, followingcorrection for system energy sensitivity (see Experimental).Prior to calculation of the linear discriminant functions toseparate the groups maximally, ANOVA was performed onthe PC weights to identify the diagnostically significantcomponents (PCs) of the spectra. Six PCs with >99.9%

significance of variance between groups were entered intothe LDA model. To separate three groups of data maximally,two LD functions are required; for greater numbers of groups(n), n � 1 linear discriminant functions are required. All thelinear discriminant ‘diagnostic models’ were tested usingcross-validation or leave-one-out procedures, whereby eachspectrum is held back in turn, the model is calculated andthe withheld spectrum is projected on to the model. Thistechnique does not provide as good a test as projectingcompletely new data on to the model, but with relativelysmall numbers of patients and samples it will provide anunbiased estimation of the model performance.

Plate 2 shows a two-dimensional scatter plot of linear dis-criminant function weights for the optimized larynx model.The resulting prediction performances for each group againstthe ‘gold standard’ histopathology are shown in Table 2. Out-come measures were calculated to allow evaluation of themodel in terms of sensitivity and specificity of in effect threetests for the larynx model: either normal versus other groups,dysplasia versus other groups or cancer versus other groups.These are also displayed in Table 2. From scatter plots ofthe linear discriminant function weights it could be seenthat group clustering was achieved successfully, althoughsome groups appeared to cluster into two sub-groups. Thisis likely to be due to the evolving spectrometer configuration



Table 1. Tentative assignments of major vibrational modes identified in oesophageal samples

Peakposition/cm�1 Major assignments

490 Glycogen524 S–S disulfide stretch in proteins621 C–C twisting mode of phenylalanine643 C–C twisting mode of tyrosine669 C–S stretching mode of cystine719 C–N (membrane phospholipid head)/nucleotide peak755 Symmetric breathing of tryptophan781 Cytosine/uracil ring breathing (nucleotide)828 Out of plane ring breathing tyrosine/O–P–O stretch DNA853 Ring breathing mode of tyrosine and C–C stretch of proline ring920 C–C stretch of proline ring/glucose/lactic acid935 C–C stretching mode of proline and valine and protein backbone (˛-helix

conformation)/glycogen1001 Symmetric ring breathing mode of phenylalanine1031 C–H in-plane bending mode of phenylalanine1083 C–N stretching mode of proteins (and lipid mode to lesser degree)1123 C–C stretching mode of lipids/protein C–N stretch1155 C–C (and C–N) stretching of proteins (also carotenoids)1170 C–H in-plane bending mode of tyrosine1209 Tryptophan and phenylalanine �(C–C6H5) mode

1240–1265 Amide III (C–N stretching mode of proteins, indicating mainly ˛-helix conformation)1313 CH3CH2 twisting mode of collagen/lipids1335 CH3CH2 wagging mode of collagen and polynucleotide chain (DNA–purine bases)1446 CH2 bending mode of proteins and lipids1548 Tryptophan1579 Pyrimidine ring (nucleic acids) and heme protein1603 C C in-plane bending mode of phenylalanine and tyrosine1616 C C stretching mode of tyrosine and tryptophan1655 Amide I (C O stretching mode of proteins, ˛-helix conformation)/C C lipid stretch

Table 2. Cross-validation classification results obtained using thediscriminant functions calculated from clinically significant principlecomponents of the larynx spectraa

Raman-predicted group membership (No.)

Classification Normal Dysplasia Carcinoma Total

Normal 100 5 7 112Dysplasia 4 34 0 38Carcinoma 7 0 42 49

Outcome measure Normal Dysplasia Carcinoma

Sensitivity/% 89 90 86Specificity/% 87 93 95

a Each case was classified by the functions derived from all cases other than thatcase; 88.4% of cross-validated grouped cases are correctly classified using thismethod.


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that was employed during measurements of the laryngealspectral dataset. Raman spectra were acquired with differ-ent resolutions, signal-to-noise ratios, calibration protocolsand laser stabilities. The use of a smoothing function to pre-process the data was shown to reduce the effect of thesevariations in the model.

OesophagusOesophageal spectra were measured from homogeneoussamples of eight different pathological groups. The meannormalized spectra from each pathological group are shownin Plate 3 for visual comparison.

The spectra were combined into clinically significantgroups for construction of a multivariate linear discrim-inant model in the same way as that described for thelarynx. However, following significant enhancement in laserand spectrometer wavenumber repeatability afforded byimprovements in temperature stabilization, it was found tobe unnecessary to smooth/pre-process the oesophageal andbladder data prior to calculation of principle components.Both eight-group and three-group linear discriminant mod-els were constructed and tested. The three-group model,relevant for biopsy targeting applications, used spectrameasured from normal, Barrett’s oesophagus and Barrett’sneoplasia. This required two linear discriminant functionsfor optimum separation of the groups. The cross-validatedLD function weights are plotted in Plate 4. However, theconstruction of an eight-group discriminant model for clas-sification necessitated the use of seven LD functions and aseven-dimensional decision space. In this case it is difficult tovisualize group separations using 2D scatter plots. However,an attempt was made to include an informative selectionto allow visualization of some grouping within two dimen-sions (Plate 7). The resulting prediction performances andoutcome measures for the three-group model are presentedin Table 3 and for the eight-group model in Table 4. Excellentseparation of samples into pathological groups was achieved

with 93.2 and 88.6% of cases correctly predicted by the three-and eight-group models, respectively.

The greatest cross-over between groups occurredbetween the intestinal metaplasia, high-grade dysplasia andadenocarcinoma groups. It is these that cause pathologiststhe greatest difficulty in classification. The cross-over is likelyto be due in part to both the difficulty in pathologicaldiscrimination between these groups and the fact thatabnormal development of the epithelial tissue follows acontinuum with many biochemical similarities.

Utilization of tentative peak assignments for significantpositive and negative peaks in the difference spectra (notshown) enabled some sense to be made of the biochemicalchanges that accompany the progress towards adenocar-cinoma in oesophageal tissue. Dysplastic and canceroustissues have been shown (tentatively) to exhibit greater DNA,hydroxyapatite, urea/phenylalanine, ˛-helix and unorderedproteins, but lower glycogen, carbohydrates, protein disul-fide bonding and carotenoids, than normal tissue. Thistends to corroborate known biochemical changes leadingto tumour develoment such as increased energy consump-tion from cell division, increased nuclear/cytoplasmic ratio,protein conformation changes and tissue calcification. Thehistopathological groupings have been developed by pathol-ogists utilizing architectural and morphological differencesto recognize from collective experience when a tissue hasundergone a change likely to be clinically significant. How-ever, cells are undergoing a continuum of change fromBarrett’s mucosa to dysplasia to adenocarcinoma, and there-fore full separation, either histologically or spectroscopically,will be difficult.

Ten principal components (1, 2, 3, 4, 5, 6, 7, 8, 10 and12) were shown to exhibit >99.9% significance of discrim-ination between the eight pathological groups designatedfrom a consensus of opinion of three pathologists. The mostsignificant PC (6) described only 0.05% of the total variance

Table 3. Cross-validation classification results obtained using the three-grouplinear discriminant model calculated from clinically significant principlecomponents of the oesophagus spectraa


Classification Normal Barrett’s Neoplasia Total

Normal 301 1 8 310Barrett’s 0 122 23 145Neoplasia 3 7 152 162

Outcome Measures Normal Barrett’s Neoplasia

Sensitivity/% 97 84 94Specificity/% 99 98 93

a Each case was classified by the functions derived from all cases other than thatcase; 93.2% of cross-validated grouped cases are correctly classified using thismethod.



Table 4. Cross-validation classification results obtained from the eight-group linear discriminant model calculated fromclinically significant principle components of the oesophagus spectraa


Classification Normal CB FB IM HGD Adeno Sq. dys SCC Total

Normal 292 0 1 4 9 0 4 0 310CB 0 12 0 0 0 0 0 0 12FB 0 0 45 2 0 0 0 0 47IM 0 0 4 66 16 0 0 0 86HGD 2 0 3 0 60 3 0 0 68Adeno 0 0 5 1 19 69 0 0 94Sq. Dys. 0 0 0 0 0 0 15 0 15SCC 0 0 0 0 0 0 0 10 10

Outcome measure Normal CB FB IM HGD Adeno Sq. dys SCC

Sensitivity/% 94 100 96 77 88 73 100 100Specificity/% 99 100 98 99 92 99 99 100

a Each case was classified by the functions derived from all cases other than that case; 88.6% of cross-validated groupedcases are correctly classified using this method.

Table 5. Cross-validation classification results obtained from the five-group lineardiscriminant model calculated from clinically significant principle components of thebladder spectraa


Classification Normal CIS LGC MGC HGC Total

Normal 14 0 0 1 0 15CIS 1 33 4 2 0 40LGC 0 2 53 0 0 55MGC 1 3 0 36 6 46HGC 0 0 1 0 39 40

Outcome measure Normal CIS LGC MGC HGC

Sensitivity/% 93 83 96 78 98Specificity/% 99 97 96 98 96

a Each case was classified by the functions derived from all cases other than that case;89.3% of cross-validated grouped cases are correctly classified using this method.

from the mean, indicating that subtle modifications in bio-chemistry precede and accompany significant pathologicalchanges to the tissue.

Although most principal components are made up of acomplex combination of tissue components, PC3 can be seento represent glycogen, which is known to be the main energysource found in human cells. Normal cells will have this inabundance whereas in abnormal cells it will be in deficit.This appears to be demonstrated in both difference spectraand principal component analyses.

BladderRaman spectra of bladder specimens were measured andgrouped according to histopathology opinion. The mean

spectra for each group are shown in Plate 5. Subtle dif-ferences can be seen. These have been exploited withthe use of a principal component fed linear discriminantmodel (as described above) to separate maximally thefive pathology groups exhibited by the specimens mea-sured. Four LD functions were calculated and the weightsof the first two are plotted in Plate 6 to demonstrategroup clustering. The cross-validation prediction perfor-mance and the outcome measures are outlined in Table 5.Excellent group separation is achieved, although there issome overlap between carcinoma in situ and the low-grade cancers and between the moderate- and high-gradecancers. This may be due in part to the biochemical contin-uum between the increasing grades of malignancy and the


572 N. Stone et al.

difficulty in distinguishing fully between these groupingswith histopathology.

Potential confounding factorsFigure 3 displays Raman spectra of potential contaminantsto tissue spectra, that may be confounding factors in spectraldiagnostic model prediction. Plots of bile, mucus, spittle

and coffee combined, blood and endoscope lubricant, allmeasured under the same conditions as the tissue spectra, areshown for comparison of signal strength with an oesophagusspectrum. The tissue spectra measured to date (withoutwashing) were not unduly affected by these contaminants,although the strengths of the signals indicate that they shouldnot be ignored from analysis altogether.

400 600 800 100012001400160018005

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400 600 800 1000 12001400 1600 18000.10.20.30.40.50.60.70.80.9

Average Oesophagus

Wavenumber / cm-1 Wavenumber / cm-1

Wavenumber / cm-1 Wavenumber / cm-1

Figure 3. Raman spectra of possible contaminants of in vivo and in vitro tissue Raman spectra, measured in 30 s and corrected forsystem energy sensitivity. An oesophagus spectrum is included for comparison.



CONCLUSIONS

This study has demonstrated the facility of Raman spec-troscopy to distinguish between the pathological state ofthree distinct epithelial tissues. This indicates the genericnature of a Raman spectroscopic tool for tissue classificationand detection of disease in different epithelial tissues.

Improvements have been made to previously publisheddata on the larynx,18 by optimizing the model using spectralfiltering prior to multivariate analysis. This effectivelyprovided a method of smoothing out the spectral variationsdue to spectrometer configuration changes and an unstablelaser wavelength. The bladder and oesophagus modelsdid not require this procedure to achieve high-qualitydiscrimination. This was due to the use of a stable,optimized spectrometer with repeatable calibration protocolsthroughout acquisition of the datasets.

This study was the first to separate spectrally so manypathology groups in the oesophagus and thus indicatethe facility of Raman spectroscopy for tissue classification,rather than just simple disease detection. Furthermore,the first example of Raman spectral separation of bladdertissue specimens into their respective pathology groups wasdemonstrated using a Raman/linear discriminant model.

There are difficulties associated with the full acceptanceof this technique as a medical tool, either for biopsy targetingor in vitro pathology classification. The use of an imperfectgold standard to calibrate and evaluate new diagnosticmodalities will always cause problems. However, this iscurrently the best technique available, and therefore theuse of strict protocols and reliance on a minimum of aconsensus of three expert histopathologists will at least tendto reduce the errors from histopathological discrepancies.Further work has been carried out to evaluate these problemselsewhere.23

The use of much greater sample numbers will be requiredto build and test models to include full inter-sample andinter-spectrometer reproducibility. All spectra measured forthis study are traceable back to histological sections andcorrection files, and therefore they can be included in anyfuture reassessment. Work is ongoing to increase samplenumbers for model construction and testing, and evaluationof the model prediction performance using heterogeneoussamples is being performed.

AcknowledgementsThe authors thank Bryan Warren and Karel Geoes, Registrypathologists, for their assistance in providing a consensus ofhistopathological opinion on the tissue specimens used in this study.The Gloucestershire Royal Hospital Histopathology Departmentprovided sample preparation and welcome advice. Bob Bennettat Renishaw deserves thanks for his collaboration and expertisein system optimization for Raman tissue measurements. Funding

was provided by MedLINK UK [DoH, Dti, Renishaw, Keymed(Olympus)].

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18. Stone N, Stavroulaki P, Kendall C, Birchall M, Barr H.Laryngoscope 2000; 110: 1756.

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Near-infrared Raman spectroscopy for the classification of epithelial pre-cancers and cancers