340 Volume 51, Number 3, 1997 APPLIED SPECTROSCOPY0003-7028 / 97 / 5103-0340$2.00 / 0q 1997 Society for Applied Spectroscopy

Multivariate Classi® cation of the Infrared Spectra ofCell and Tissue Samples*

DAVID M. HAALAND,² HOWLAND D. T. JONES, and EDWARD V. THOMASSandia National Laboratories, Albuquerque, New Mexico 87185-0342

Infrared microspectroscopy of biopsied canine lymph cells and tis-sue was performed to investigate the possibility of using IR spectracoupled with multivariate classi® cation methods to classify the sam-ples as normal, hyperplastic, or neoplastic (malignant). IR spectrawere obtained in transmission mode through BaF2 windows and inre¯ ection mode from samples prepared on gold-coated microscopeslides. Cytology and histopathology samples were prepared by avariety of methods to identify the optimal methods of sample prep-aration. Cytospinning procedures that yielded a monolayer of cellson the BaF2 windows produced a limited set of IR transmissionspectra. These transmission spectra were converted to absorbanceand formed the basis for a classi® cation rule that yielded 100%correct classi® cation in a cross-validated context. Classi® cations ofnormal, hyperplastic, and neoplastic cell sample spectra wereachieved by using both partial least-squares (PLS) and principalcomponent regression (PCR) classi® cation methods. Linear discrim-inant analysis applied to principal components obtained from thespectral data yielded a small number of misclassi® cations. PLSweight loading vectors yield valuable qualitative insight into themolecular changes that are responsib le for the success of the infra-red classi® cation. These successful classi® cation results show prom-ise for assisting pathologists in the diagnosis of cell types and offerfuture potential for in vivo IR detection of some types of cancer.

Index Headings: Multivariate IR classi® cation; PLS, PCR; Discrim-inant analysis; Classi® cation of cells and tissue; FT-IR; Cancer de-tection.

INTRODUCTION

The classi® cation of biopsied human or animal cell andtissue samples as normal, hyperplastic, dysplastic, or neo-plastic (i.e., malignant or cancerous) is normally made bythe visual examination of stained samples under the mi-croscope. The process of preparing the samples requirestrained technicians, while the examination and classi® -cation of the samples demands licensed pathologists.Biopsied tissue obtained during surgery often must beevaluated rapidly by the pathologist for decisions to bemade about more radical surgical intervention. In addi-tion, the less invasive small-needle aspiration methodsremove a small number of cells that do not have thepresence of surrounding cells and tissue structure forcomparison to aid the pathologist in the classi® cations.Thus, in many cases, cell and tissue classi® cations mustbe made in dif® cult or ambiguous situations.

Recently, a number of groups have shown that infrared(IR) spectroscopy of cells and tissue conveys informationthat can be helpful in aiding the pathologist in the clas-si® cation of cell and tissue samples. Groups have shown

Received 4 April 1996; accepted 4 October 1996.* This work was performed at Sandia National Laboratories and sup-

ported by the U.S. Department of Energy under Contract DE-AC04-94AL85000.

² Author to whom correspondence should be sent.

that IR spectroscopy can help to distinguish normal andleukemic lymphocytes and lymphoblasts,1,2 normal andneoplastic lung tissue,3 and normal and cancerous colo-rectal cells.4± 7 Medullary carcinoma of the thyroid hasbeen detected with IR,8,9 and classi® cation of normal,dysplastic, and cancerous cells of the cervix has beenaccomplished with the aid of infrared spectroscopy.10± 12

Very recent studies have been conducted that showmarked differences in the IR spectra of normal and car-cinoma breast tissue13 and the IR of DNA extracted frombreast tissue.14 The infrared spectra are sensitive to thedifferences in DNA and protein content of the cells3 aswell as differences in protein structure8 and degree ofmethylation.4 Differences in the pressure response of theIR spectra of normal and neoplastic cells have also beenreported.4,5,7,10,11 In addition, IR microspectroscopy can beused to examine small numbers of cells in cytologicaland histopathology samples. In all but the most recent12± 14

of these works, univariate methods involving ratios of IRpeak intensities or shifts in band positions have been usedin the spectral classi® cation of the samples.

Changes in the IR spectrum of a sample are often sub-tle but are exhibited throughout many regions of the spec-trum. Therefore, more powerful multivariate calibrationmethods15± 17 adapted for multivariate classi® cation ormore traditional multivariate classi® cation methods18 canincrease the sensitivity and reliability of IR spectral clas-si® cation of cell and tissue samples. Meurens et al.13 haveused partial least-squares (PLS) multivariate calibrationmethods over narrow IR spectral regions to predict thevolume density of malignant cells in breast tissue. In ad-dition, Ge et al.12 have demonstrated the use of multi-variate principal component analysis and discriminantanalyses to classify near-infrared (NIR) spectra of Papsmears as belonging to normal, atypical, or malignant celltypes. Because NIR has the advantage of greater pene-tration depths, NIR might be used for classi® cation oftissue noninvasively (e.g., to detect skin cancer) or withthe use of minimally invasive ® ber-optic methods (e.g.,to detect cervical or colorectal cancers or other cancersthat can be accessed with ® ber-optic sensors).

In this paper, we present mid-IR data on cytology andhistopathology samples using canine lymphoma as ourtest model. We show that multivariate classi® cations canbe made by using infrared microscopy and the multivar-iate calibration methods of partial least-squares19,20 andprincipal component regression (PCR),19,21,22 used in amode adapted for classi® cation. In addition, linear dis-criminant analysis12,18 methods have been used for mul-tivariate classi® cation. The discrimination rules devel-oped during the classi® cation development can be madeto distinguish among normal, hyperplastic (i.e., normal

APPLIED SPECTROSCOPY 341

FIG. 1. Mid-infrared spectrum of 100- m m-diameter regions of caninelymphoma cells on BaF2 windows prepared by the cytospinning tech-nique.

but rapidly replicating cells), and neoplastic cells. Exten-sions of these methods to other neoplastic cells and tis-sues and to NIR spectra for in vivo determinations arealso discussed.

EXPERIMENTAL

Normal, hyperplastic, and neoplastic lymph cells andtissues were obtained from dogs being treated at the Uni-versity of Purdue School of Veterinary Medicine. Cytol-ogy and histopathology samples were prepared by a va-riety of methods in several con® gurations to identify theoptimal methods of sample preparation. Cytology sam-ples were initially prepared by obtaining small-needle as-pirations from lymph nodes or by making impressionsmears of the biopsied lymph nodes. All cell sampleswere placed on both BaF2 infrared transparent windowsfor IR transmission measurements and on gold-coatedslides for IR re¯ ection spectra. Cytology samples wereexamined either with no further preparation or after sep-arate samples had been stained with Wright’s stain23 orDiff-quik stain.23 The histopathology samples were ob-tained from thin (microtomed to 1 m m) sections of biop-sied tissue in paraf® n and placed on BaF2 windows andgold-coated slides. These histopathology samples wereexamined without further preparation, after the sampleswere ® xed in ethanol, or after they had been ® xed andstained with H and E stain.24 All stained samples wereclassi® ed as normal, hyperplastic, or neoplastic by a pa-thologist at the University of Purdue. Samples with allthe above sample preparation methods were obtainedfrom 16 dogs over a period of six months. However, asdiscussed in the Results section, the within-sample vari-ations of these spectra were as large as the spectral vari-ations between samples from the three classes of cells.Therefore, a second set of samples from 12 dogs wasprepared by using a monocellular dispersion obtainedfrom a cytospin technique.3 This cytospin preparationproved to be a very successful and reproducible prepa-ration method and was used as the only sample prepa-ration method for the 12 dogs sampled over the remain-ing ® ve months of the study. Therefore, only the classi-® cation results for the spectra obtained from the samplesinvolving the 12 dogs (36 spectra total) with the use ofthe cytospin preparation method are reported.

The samples were transported overnight to Sandia Na-tional Laboratories, where they were examined within 24h of preparation with a Nicolet 800 Fourier transforminfrared spectrometer coupled to a Spectra-Tech IR Planredundant-apertured microscope. Spectra were collectedat 8-cm2 1 resolution with a 100-m m aperture. A liquidnitrogen-cooled Hg/Cd/Te detector with a 100-m m-di-ameter detector element was used for obtaining the IRsignal from 700 to 4000 cm2 1. Samples on the BaF2 win-dows were examined in transmission mode, while thesamples on the gold-coated microscope slides were ob-tained in re¯ ection. Signal-averaging of 512 scans wasused for each sample spectrum. The visual microscopeimage was used to isolate 100-m m-diameter regions ofhomogeneous material. At least three spectra from dif-ferent regions were obtained from each sample from eachdog. Spectra were transferred to a 486 compatible PC anda DEC 8650 VAX computer for analysis.

Spectral data preprocessing included subtraction of thewater-vapor spectrum and linear baseline corrections overwell-de® ned regions (950± 1725 and 2830± 3000 cm2 1), fol-lowed by normalization of each spectral region to the max-imum absorption band in that spectral region. A variety ofspectral regions were examined separately or in combina-tion in order to obtain optimal classi® cation accuracy. Dis-crimination rules were developed by using PLS, PCR, andlinear discriminant analysis. PLS and PCR, normally usedfor calibration, were adapted for classi® cation by setting thevalue of normal cells as 0, hyperplastic as 0.5, and neo-plastic as 1.0. In general, PLS and PCR cannot be used todiscriminate between more than two classes because of thedif® culty in determining the relative quantitative values toassign to the classes when more than two classes are pos-sible. However, in this case, it is known that the hyperplasticcells represent an intermediate stage that is between normaland neoplastic. Therefore, by assigning to the hyperplasticcells a quantitative value between that of normal and neo-plastic, the third class of cells could be included in the PLSand PCR analysis. In this manner, classi® cations could bebased on the prediction closest to the value representing thethree classes. The success of the three-way classi® cation bythese methods is evidence of the validity of this approachin this particular case. Since the number of dogs was small,cross-validation techniques leaving one sample (or one dog)out at a time were used to assess classi® cation performance.Because cross-validation was also used to establish the cor-rect model size, their assessed classi® cation performancecould be somewhat optimistic relative to a truly indepen-dent validation set of samples.

Linear discriminant analysis was performed by ® rstcompressing the spectral data to scores (principal com-ponents) by principal component analysis methods. Thescores were then used in a linear discriminant analysisby using Mahalanobis distances as the criteria for clas-si® cation.25

RESULTS AND DISCUSSION

Figure 1 shows the absorbance infrared spectrum of a100-m m area of a sample prepared on a BaF2 windowwith the cytospinning technique. The dashed portion ofthe spectrum has low information content and was not

342 Volume 51, Number 3, 1997

FIG. 2. Comparison of mid-infrared microspectroscopy of canine nor-mal, hyperplastic, and neoplastic lymph cells in the CH stretching re-gion of the spectrum. Spectra were baseline-corrected and normalizedin this region of the spectrum.

FIG. 3. Comparison of mid-infrared microspectroscopy of canine nor-mal, hyperplastic, and neoplastic lymph cells in the ® ngerprint regionof the spectrum. Spectra were baseline-corrected and normalized to themaximum peak in this region of the spectrum.

included in any of the classi® cation analyses. The base-line-corrected and normalized spectra in the CH2 and CH3

stretching region for normal, hyperplastic, and neoplasticsamples are presented in Fig. 2. Spectra of the same sam-ples in the ® ngerprint region are given in Fig. 3. Thespectral region between 2800 and 3000 cm2 1 is the CHstretching region of saturated hydrocarbons largely rep-resenting lipids and proteins.4 In the ® ngerprint region,the spectra are primarily representative of proteins, al-though the band structure is modi® ed by the presence ofDNA, collagen, and possibly other components. The twostrong bands centered at 1650 and 1540 cm2 1 representprimarily the amide I and amide II bands of protein,3,26

respectively. The CH2 scissoring vibration is found at1467 cm2 1.4,6,27 The band near 1450 cm2 1 is the asym-metric CH3 bending vibration.6 The band near 1400 cm2 1

has been assigned to a symmetric carboxylate vibration.26

The bands at 1240 and 1085 cm2 1 have multiple sources.The protein components can be assigned to the amide IIIband of proteins with disordered structure26 and to car-bohydrate vibrations of glycoproteins,27 respectively.These two bands also have contributions due to DNA andrepresent the asymmetric and symmetric PO2

2 stretchingvibrations of the phosphodiester backbone of nucleic ac-ids,4,6,14 respectively, that also occur at 1240 and 1080cm2 1 (or 1224 and 1087 cm2 1, as described in Ref. 27).However, in DNA, the 1240-cm2 1 band is generally onlyone third the intensity of the 1080-cm2 1 band,14 whilethese bands have comparable intensity in Fig. 3. The1240-cm2 1 band may also have contributions arising fromcollagen.27 The weak band at 970 cm2 1 has variouslybeen assigned to the O± P± O antisymmetric stretchingmode of DNA1 or to a phosphate monoester band ofphosphorylated proteins and nucleic acids.13 Shoulders onthe low-energy side of the 1080-cm2 1 band at 1025 and1047 cm2 1 are of uncertain origin but have been assignedto the vibrational modes of glycogen11 or to DNA vibra-tions6 by Wong et al. There is a very weak band at 994cm2 1 that we generally observed only in the neoplasticsample. This band has been assigned by Wong et al.6 toan RNA vibration, but this assignment should be consid-ered tentative.

Although differences are seen in the spectra presented

in Figs. 2 and 3, some of the differences observed inthese ® gures are simply due to sample-to-sample varia-tions rather than consistent differences across classes.

The IR spectra of cytology and histopathology samplesthat were ® xed or ® xed and stained were also studied toexplore the possibility of developing discrimination rules.However, for these samples, the within-sample spectralvariation observed was large relative to the class-to-classspectral variation. Thus, 100% accurate discriminationrules could not be developed for these samples. Relative-ly large within-sample spectral variation was also ob-served for the un® xed histopathology samples and for theun® xed aspiration and impression smear cytology sam-ples. Therefore, only the un® xed cytospun samples wereobserved to have suf® ciently small within-sample spec-tral variations to allow for the development of accuratediscrimination rules for these normal, hyperplastic, andlymphoma samples with only minute differences in theirspectra. The small within-sample spectral variation of thecytospun samples is due to the more uniform nature ofthese monocellular layer cytology preparations.

It should be noted that un® xed cells are subject tochanges with time. Since we made the ® rst infrared mea-surements the day after the samples were collected, therewas some concern that changes may have taken placeduring the initial 24-h period that it took to receive thesamples. Therefore, the spectra of several samples werefollowed as a function of time for 5 days. These studiesindicated that the spectra of the samples were relativelyconstant with time with noticeable changes becoming ap-parent only during the ® fth day. Therefore, time-depen-dent changes in the ® rst 24 h of sample preparation donot appear to in¯ uence these sample spectra that wereconsistently collected the day after preparation.

The spectral differences that we observed between nor-mal, hyperplastic, and neoplastic cells were not nearly aslarge as those reported in the literature for normal andneoplastic cells obtained from other types of canceroustissue or cells. This observation may be a result of asmaller differentiation among the lymph cells withchanges to malignancy than is found with the differenttissue types. In particular, consistent differences betweennormal and malignant tissues are observed visually in the

APPLIED SPECTROSCOPY 343

FIG. 4. Classi® cation results based upon the mid-infrared spectra byPLS using cross-validation leaving one spectrum out at a time. Bound-ary lines drawn represent the mid-point classi® cation boundaries be-tween the three classes of cells (i.e., normal, hyperplastic, and neoplas-tic).

FIG. 5. Classi® cation results based upon the mid-infrared spectra byPLS using cross-validation leaving one dog’s set of spectra out at atime. The solid line represents the mid-point classi® cation boundarybetween the hyperplastic and neoplastic classes of cells.

infrared spectra presented in the literature. Few consistentspectral differences are observed visually between thenormal, hyperplastic, and neoplastic lymph cells exam-ined in this study, making this IR cell classi® cation moredif® cult than the IR classi® cation of other cancers.

The classi® cation results for PLS are presented in Fig.4 for samples prepared by the cytospin method and de-posited on BaF2 windows. This PLS classi® cation usedthe spectral regions 950± 1725 and 2830± 3000 cm2 1. Inthis case, the quantitative response values 0, 0.5, and 1represent normal, hyperplastic, and neoplastic cell types.PLS was then used to model these quantitative responsevariables as a function of the spectral measurement. Thehyperplastic cells were arbitrarily given the value of 0.5on the basis of the observation of pathologists that thehyperplastic cells represent structural changes that are be-tween normal and neoplastic cells. Other values between0 and 1 were tested for the quantitative response valueof the hyperplastic cells. All values between 0.25 and 0.7for hyperplastic cells gave the same 100% classi® cationaccuracy, but the separation between classes was greatestwhen this value was set to 0.5. The data in Fig. 4 are theresult of cross-validated calibration leaving out one spec-trum at a time for each of the three replicate samplesfrom the 12 dogs. It is clear from Fig. 4 that the threeclasses of cells are completely separated, as indicated bythe fact that all spectra of a given class fall between thedividing lines drawn between classes. Thus, spectra witha prediction below 0.25 are classi® ed as normal, spectrapredicted between 0.25 and 0.75 are hyperplastic, andspectra predicted above 0.75 will be classi® ed as neo-plastic. The actual mid-points of the classi® cation resultsshown in Fig. 4 were 0.32 and 0.72. However, these lattervalues may simply be due to the small sample size, andthe more heuristic values of 0.25 and 0.75 are chosen forthe discrimination rules.

A more robust classi® cation procedure would performthe PLS cross-validated classi® cation rotation by sepa-rately leaving out the set of spectra for each dog. Unfor-tunately, samples were available only from one dog thatwas classi® ed as normal. Therefore, cross-validationleaving out one dog at a time would mean extrapolation

of neoplastic and hyperplastic cell spectra to normal cellspectra, which is not possible. With the use of only thesamples that had been classi® ed by the pathologist ashyperplastic or neoplastic, a PLS-based discriminationrule was developed by cross-validation leaving out onedog’s spectra at a time. The results of this cross-validatedPLS classi® cation are presented in Fig. 5, showing 100%classi® cation success. The data presented in Fig. 5 werebased upon a classi® cation model with ® ve PLS factors.However, reducing the model to four PLS factors stillresulted in 100% classi® cation success. This observationprovides evidence that accurate classi® cation can be ob-tained, prospectively, with new dogs, since the multiplespectra from each dog did not remain in the calibrationmodel even when the candidate cell classes were similar.PCR-based classi® cations yielded similar results as pre-sented here for the PLS-based classi® cations.

Classical linear discrimination analysis was also usedto develop discrimination rules for the canine lymphomadata. Brie¯ y, linear discriminant analysis ® nds the lineardiscrimination function that maximizes the among-groupdistances relative to the within-group distances. Using thelinear discrimination methodology directly on the spectrais not advisable because of the large number of spectraldata points used in the spectra, which makes the discrim-inant analysis computationally intensive and less reliable.This decreased reliability is due to the high degree ofcolinearity among measurements at different frequencies.Thus, principal component analysis was ® rst applied toall the spectra to limit the variables to a small number oforthogonal variables (i.e., the principal components orscores). The number of principal components (h) impor-tant for discrimination is selected by cross-validation.The h-dimensional average vector of scores and h 3 hdimensional covariance matrix of scores are computed foreach group. Then the average within-group covariancematrix (S) is calculated. For the classi® cation of the cellsof an unknown sample, the h-dimensional score vector,t, is calculated from the measured spectrum and the ei-genvectors determined in the initial calibration of spectrafrom cells of known classi® cation. The distances from tto the average score for each group are computed andnormalized by S. The decision rule for classifying a sam-

344 Volume 51, Number 3, 1997

FIG. 6. Classi® cation results based upon the mid-infrared spectra ofcanine cells by multivariate discriminant analysis using principal com-ponent analyses and Mahalanobis distances and cross-validation leavingout one dog’s three repeat spectra at a time. The solid line representsthe boundary between hyperplastic and neoplastic cells.

FIG. 7. The normalized ® rst PLS weight loading vector for the clas-si® cation of hyperplastic and neoplastic canine lymph cells (solid line)for the spectral region shown and the average absorbance spectrum ofboth classes of cells (dashed line, in absorbance units displaced by 0.1AU).

ple as belonging to a given group, i, is obtained by se-lecting the group that has the minimum Mahalanobis dis-tance to the spectrum of the unknown sample. Whencross-validation was employed prospectively across dogs,two of the neoplastic spectra (one from each of two dogs)were not classi® ed correctly. This result compares withthe 100% success with the PLS- and PCR-based ap-proaches. The best discriminant classi® cation used ® veprincipal components (numbers 1, 2, 4, 5, and 7), and theresults of this discriminant analysis are shown in Fig. 6.Again, since samples from only one normal dog wereavailable, inclusion of the normal dog spectra in the clas-si® cation was not possible. It is possible that this ap-proach was not as successful as the other approaches forthis case because the classi® cation was based upon theassumption that the within-class variances are equal. Giv-en the relatively small sample size in this study, we areunable to reject the assumption of equal within-class var-iances. However, it is not likely that these within-classvariances are equal, since it has been shown in leukemiclymphocytes that the molecular structure of neoplasticcells is less variable relative to that of normal cells.14,28

A potential advantage of using the classical discrimina-tion technique is the ability to incorporate misclassi® ca-tion costs as well as prior estimates of the proportions ofeach class in the population in a decision-theoretic frame-work to de® ne the discrimination rules. For example,false negatives in cancer detection are more detrimentalthan false positives. Therefore, a greater cost would beassociated with the occurrence of a false negative. Theenhancement of the discrimination rules with misclassi-® cation costs and prior estimates of the proportion ofeach class in the population also awaits a large samplepopulation to con® rm the usefulness of these factors fordiscrimination.

The infrared spectra of the various cell types offer thepossibility of obtaining qualitative information about themolecular structure responsible for the successful discrim-ination between hyperplastic and neoplastic cells. An initialexamination of the spectral differences was made by com-

paring the average spectra obtained separately from each ofthe three classes. However, some of the differences foundby this method were not consistently useful for classi® ca-tion. For example, the relative amount of methylation wasgreater for the neoplastic cells relative to normal cells. Hy-perplastic cells averaged more methylation than the neo-plastic cells. These observations are also apparent in Fig.2. However, these differences were not reliably useful fordiscrimination, since the classi® cation using only the CHstretching region was unsuccessful.

On the other hand, classi® cations between hyperplasticand neoplastic cells were successful in the ® ngerprint regionfrom 950 to 1725 cm2 1. It is bene® cial to use the successfulPLS-based classi® cation to guide understanding of the mo-lecular structure differences which allow for successful clas-si® cation. It has been previously shown20 that the ® rstweight loading vector in the PLS analysis often yields themost useful qualitative information. That characteristic ap-pears also to be the case in this multivariate classi® cationproblem. The ® rst weight loading vector associated with thePLS model used to classify hyperplastic and neoplastic cellsis given in Fig. 7 along with the average spectrum of thesetwo classes of cells.

Remembering that the spectra have been individuallynormalized to the 1650-cm2 1 protein band, the positivefeatures in the weight loading spectrum represent thosefeatures that are relatively more important for positiveclassi® cation of neoplastic cells. The most prominent fea-ture in the weight loading vector is the 1635-cm2 1 bandon the side of the amide I band. This band is consistentwith a beta-sheet protein structure.3 In addition, the pres-ence of bands centered at 970, 1085, and 1240 cm2 1 isconsistent with relatively more DNA in the neoplasticcells. There is structure to these bands that is differentfrom that found for the average spectrum. For example,the 1240-cm2 1 band becomes a doublet at 1220 and 1242cm2 1 in the loading vector. Also, the central portion ofthe 1085-cm2 1 band is relatively enhanced. If the relativeamount of DNA has increased in the neoplastic cells, thenwe would expect the 1085-cm2 1 band to increase relativeto the 1240-cm2 1 band, since this 1085-cm2 1 band ismore intense than the 1240-cm2 1 band in DNA.14 The1220-cm2 1 band that is apparent in the PLS weight load-

APPLIED SPECTROSCOPY 345

ing vector may actually represent a DNA vibration, andthe 1242-cm2 1 band may be due to collagen, as claimedby Mantsch and Jackson.27 The very weak band at 994cm2 1 that appears in neoplastic cells is accentuated in thisweight loading vector. The high-frequency structure inthe spectral region from 1350 to 1725 cm2 1 is due toincomplete correction for water vapor changes in thepurge gas and is not representative of any samplechanges. These water vapor features may obscure someof the weaker features in this spectral region. Finally,there is a shoulder at 1562 cm2 1 on the high-energy sideof the 1550-cm2 1 amide II band that appears in the weightloading spectrum. This band is consistent with the asym-metric carboxylate band that is expected to be presentwith the 1400-cm2 1 symmetric carboxylate band. It couldalso possibly represent a change in the tertiary proteinstructure for the neoplastic cells. Thus, it is clear that thequalitative information available in the weight loadingvectors that are generated in the PLS classi® cation pro-cess is valuable for understanding the molecular structuredifferences between these classes of cells.

CONCLUSION

It is clear from this study that mid-infrared spectros-copy coupled with multivariate classi® cation methods canprovide a useful tool to aid the pathologist in the classi-® cation of normal, hyperplastic, or neoplastic lymphcells. The high rates of correct classi® cation (100% forPLS- and PCR-based discrimination) when cross-valida-tion is used provide evidence of the usefulness of thesemethods. It had originally been hoped that the use ofmultivariate classi® cation techniques would allow morestandard cell and tissue preparation methods to be usedin the infrared classi® cation of the samples. Unfortunate-ly, it was found with the canine lymphoma model thatthe spectral differences were so small between the threecell classes that the more specialized cytospin cell prep-aration was required in order to obtain accurate classi® -cations. It is possible, as indicated in the literature cited,that cancers other than canine lymphoma may be moredifferentiated. Therefore, it might be possible to classifycells and tissue for these more differentiated cancers onthe basis of the multivariate classi® cation of infraredspectra of samples prepared by the more routine prepa-rations involving ® xed and/or stained cytology and his-topathology samples.

In the area of potential future developments, mid-in-frared spectroscopy with the use of mid-infrared ® ber-optic probes could be used for in vivo detection of can-cers on the surfaces of tissue. These might include thedetection of skin, cervical, uterine, colorectal, mouth,throat, esophageal, or stomach cancers. Ge et al.12 haveshown with Pap smears that successful cell classi® cationscan be made by NIR spectroscopy. It is therefore possiblethat near-infrared spectroscopy coupled with ® ber-opticprobes could be used for deeper sampling of tissue. De-pending on the wavelength range used, the depth of pen-etration of near-infrared radiation in tissue could be afraction of a millimeter to more than 10 mm. Thus, can-cers that are more than skin deep might be detected witha near-infrared in vivo ® ber-optic probe. Detection of thecancers listed above would be possible as well as prostate

and other cancers that could be sampled within 1 cm ofthe ® ber-optic probe. Fiber-optic probes in the mid- ornear-infrared spectral ranges could also be used duringbiopsies to achieve rapid classi® cations of tissue and cellsduring the surgical procedure.

Attenuated total re¯ ection (ATR) methods could alsobe employed in the mid-infrared to potentially increasesensitivity of in vitro cytology and histopathology sam-pling, or during in vivo surface sampling. An infraredmicroscope coupled with an ATR objective could be usedfor in vitro cytology and histopathology samples.

ACKNOWLEDGMENTS

We would like to acknowledge the services of D. B. DeNicola andP. L. Bonney of the University of Purdue School of Veterinary Medi-cine. P. L. Bonney prepared most of the cytology and histopathologycanine lymph samples, while D. B. DeNicola prepared the cytospunsamples. Dr. DeNicola was the licensed pathologist who made all theclassi® cations of the samples as normal, hyperplastic, or neoplastic.

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