Factor Analysis of Cancer FourierTransform Infrared Evanescent Wave

Fiberoptical (FTIR-FEW) SpectraSydney Sukuta, MA* and Reinhard Bruch, PhD

Department of Physics, University of Nevada Reno, Reno NV 89557

Background and Objective: The purpose of this study is to iso-late pure biochemical compounds’ eigenspectra and to classifyskin cancer tumors.Study Design/Materials and Methods: Fourier transform infra-red fiberoptic evanescent wave (FTIR-FEW) spectra, in themiddle infrared (MIR) region, of human normal skin tissue andcancer tumors were analyzed using chemical factor analysis.Results: Eigenspectra of biochemical species were isolated andsome of the eigenspectra have been preliminarily identified asdue to protein peptide bond and lipid carbonyl vibrations. Clus-ter analysis was used for classification and good agreementwith prior pathological classifications, specifically for normalskin tissue and melanoma tumors, has been found. However thecluster analysis suggests substantial variability in basaloma tu-mor biochemical characteristics. In addition this study hasdemonstrated that chemical factor analysis can be carried outdirectly on raw data to extract biochemical componenteigenspectra and classify skin states. Most importantly, it hasbeen demonstrated that the combination of FTIR-FEW tech-nique and chemical factor analysis has potential as a clinicaldiagnostic tool. Lasers Surg. Med. 24:382–388, 1999.© 1999 Wiley-Liss, Inc.

Key words: biochemical factor analysis; biochemical component isolation; cancerclassification.

INTRODUCTION

With recent advances in fiberoptic technol-ogy Fourier transform fiberoptic evanescent wave(FTIR-FEW) spectroscopy has become a powerfulnew method for human skin tissue analysis invivo [1–4]. Specific biochemical constituents of tis-sue can be used to reflect the health or diseasestate of patients when probed spectroscopically[1–8]. Unlike conventional methods, FTIR-FEWspectroscopy, in the middle infrared (MIR), probestissue biochemistry at a molecular level [1–4] andthe observed MIR spectra exhibit superimposedor composite vibrational bands. It is thereforestrategic for FTIR-FEW spectroscopy to employadvanced computational methods for analysis andinterpretation of obtained spectra.

Recently skin disease classification has been

done using neural networks by Ma and co-workers [9–10]. They used an unsupervised com-petitive neural network to distinguish betweennormal skin tissue, benign, and melanoma tu-mors. The corresponding FTIR-FEW spectra havebeen analyzed without any feature extraction.This network has high efficiency and good reli-ability. However these type of pattern recognitiontechniques [9–12] are mathematically abstractand do not take advantage of the specific bio-chemical information contained in the MIR spec-tra.

The aim of this study is to assess the useful-

*Correspondence to: Sydney Sukuta, Department of Physics,University of Nevada Reno, Reno NV 89557.E-mail: sydney@physics.unr.edu

Accepted 26 January 1999

Lasers in Surgery and Medicine 24:382–388 (1999)

© 1999 Wiley-Liss, Inc.

ness of FTIR-FEW spectroscopy method [1–4]combined with chemical factor analysis [13] todiscriminate between different types of skin can-cer and isolate eigenspectra of active biochemicalspecies. In this work some of the eigenspectra ex-tracted by chemical factor analysis have been pre-liminarily identified as due to peptide bonds andlipid carbonyls. The isolation of biochemicaleigenspectra suggests the possibility of monitor-ing specific biochemicals participation in the car-cinogenic activity. The classifications were inagreement with pathological preclassifications fornormal skin tissue and melanoma tumors, how-ever basaloma tumors’ cluster points were not lo-calized. This implies that cluster analysis has thepotential to detect and classify normal skin tissueand melanoma tumors.

MATERIALS AND METHODS

Experimental Method

FTIR-FEW spectroscopy has been performedon normal skin, basaloma and melanoma tumorsurfaces. The experimental setup, shown in Fig-ure 1, consists of an FTIR spectrometer, nitrogen-cooled mercury cadmium telluride, MCT, detectorand optical fibers that operate in the attenuatedtotal reflection (ATR) [14] regime. The optical fi-bers used are nontoxic.

Data is acquired noninvasively by putting

the optical fiber in contact with the surface of in-terest and the infrared radiation penetrates intothe tissue surface probing tissue biochemistry ata molecular level. The optical penetration depthdp [14], defined as the distance necessary for theoptical field amplitude to fall to e-1 of its value atthe surface, is expressed as:

dp = lf$2p@sin2~u! − ~ns/nf!2#1/2%−1 (1)

where lf is the radiation wavelength in the opticalfiber, nf is the fiber index of refraction, ns is theskin index of refraction, and u is the angle of in-cidence of the optical fiber. The measured spectraalso depends on the contact total surface areapressure. Contributions from as deep as five opti-cal penetration depths have been reported else-where [15].

After the radiation interacts with the surfaceof interest, the optical fiber system delivers thesignals to the MCT detector, which then trans-mits them electronically to the spectrometerwhere they are processed by an internal computerand stored on disk for analysis. Data presented inthis study were recorded in the 1,480–1,800 cm−1

spectral range, and it took about 40 seconds torecord each spectrum.

Chemical Factor Analysis

In this study computer-based chemical factoranalysis is used to classify and isolate pure bio-chemical eigenspectra of melanoma and basalomatumors, and normal skin tissue. A brief descrip-tion of pertinent aspects of chemical factor analy-sis used in this investigation is given below. Thetheory is comprehensively treated elsewhere[13].The analysis involves modeling a covariancestructure of a data matrix [A], from which datamatrix factors or eigenvectors and their corre-sponding eigenvalues are extracted. According toBeer’s law the absorbance, Aik, per unit length ofmixture k at wavelength i obeys the sum of prod-uct terms:

Aik = (j=1,n eijcjk (2)

where eij is the molar absorptivity per unit path-length of component j at wavelength i, and cjk isthe molar concentration of component j in the kth

mixture.The first component or factor has the largest

eigenvalue and accounts for the greatest variancein the data matrix, the second factor is orthogonal

Fig. 1. Fourier Transform Infrared Fiberoptical EvanescentWave (FTIR-FEW) spectroscopy experimental setup.

Factor Analysis of Cancer FTIR-FEW Spectra 383

to the first factor and has the second largest ei-genvalue. Subsequent factors are orthogonal to thepreceding ones and are of decreasing importance.The Malinowiski criterion [13] has been applied todetermine the total number, n, of principal factorsresponsible for spectral data. The n principal fac-tors are retained and used to extract their associ-ated n eigenspectra and/or to construct an n-dimensional factor space for cluster analysis orclassification. The remaining, j minus n, factorsare noise factors, hence discarded.

Chemical factor analysis algorithms [13]were used in the MATLAB (The MathWorks,Natick, MA) computing and visualizing environ-ment. No prior knowledge about the biochemistryof each patient was used in the computationshowever the principal factors extracted are inti-mately connected to the patients’ biochemistry. Inour view chemical factor analysis represents anonsubjective way to extract diagnostic features.

RESULTS

Experimental Results

Eighteen FTIR-FEW spectral data files,spanning from 1,480 cm−1 to 1,800 cm−1, were col-lected altogether. Six data files were each col-lected from six different normal individuals’ skinsurfaces while one, two, and three data files, to-taling six data files, were each collected fromthree individuals’ basaloma tumor surfaces re-

spectively, and a total of six melanoma data fileswere all collected from two different individuals’tumor surfaces where three data files were eachcollected from each individual.

Chemical Factor Analysis Results

Three data matrices of six rows by 320 col-umns (6 × 320) each, were constructed from theexperimental data, where each matrix is com-posed of data from each of the three skin statesunder study. Each matrix row, or each absorbencyspectrum, was normalized to one before analysisby chemical factor analysis.

B-1 Determination of the Factor Space Size

The first step in chemical factor analysis isthe determination of the number, n, of principalbiochemical factors in a data matrix [A] . To de-termine the number of principal factors we haveused the Malinowski indicator function (IND) andsignificance level (%SL) [13] as shown in Figure2a and b. The Malinowski significance level (%SL)is read from the highest factor, n is where thesignificance level drops to 5%, while the indicatorfunction (IND) reaches a minimum in the vicinityof the correct n. We have shown the factors andtheir associated eigenvalues for the different skinstates in Table 1. Two biochemical principal fac-tors were determined as responsible for 93.23%and 5.20% of the eigenvalues or variance for ba-saloma tumors using the Malinowski criterion,

Fig. 2. a: Characteristic plot of the in-dicator function versus number of bio-chemical factors. This reaches a mini-mum at n 4 2 corresponding to the op-timum number of biochemical factors.b: Characteristic plot of the significancelevel (%) versus number of biochemicalfactors. The arrow indicates the opti-mum number of biochemical factors.

384 Sukuta and Bruch

see Figure 2a and b. Normal skin tissue and mela-noma tumors each exhibit two principal biochemi-cal factors as responsible for the data. These twofactors account for 99.44% and 0.47% for normalskin tissue, and 99.17% and .66 % for melanomatumors, respectively (see Table 1). Therefore twoprincipal factors, for each case, were used in theanalysis of the spectral isolation model.

B-2 Spectral Isolation

The experimental FTIR-FEW spectral datafor normal skin, basaloma and melanoma are ex-hibited in Figures 3a, 4a, and 5a, respectively.The results of the spectral isolation are shown inFigures 3b, 4b, and 5b for normal skin tissue, ba-saloma and melanoma tumors, respectively. Theisolated eigenspectrum labeled A corresponds tothe first (n 4 1) principal biochemical factor, andB corresponds to the second (n 4 2) principal bio-chemical factor, and curve C (+) represents thesum or composite spectrum of A and B in all fig-ures. Specifically Figure 3b shows the isolatedeigenspectra for normal skin, whereas Figure 4bis associated with basaloma tumors and Figure 5bis associated with melanoma. The composite spec-tra, curve C, demonstrate that two principal fac-tors are sufficient to characterize each skin stateinvestigated. Figure 6a, b, and c exhibit the iso-lated eigenspectra, A and B, and their correspond-ing composite spectra, curve C, for normal skintissue, basaloma and melanoma tumors respec-tively.

B-3 Cluster Analysis

Two principal biochemical factors were ex-tracted from the covariance matrix of all the 18FTIR-FEW spectral data files under study. Theprojection of the 18 spectra onto the two-dimensional principal biochemical factor space isshown in Figure 7, where each point representsone spectrum. The normal skin and melanoma tu-mor spectral points are well localized or clusteredand their clusters are separated from each otherwhereas basaloma tumor spectral points are notconfined at all.

DISCUSSION

This study demonstrates that chemical fac-tor analysis can be used to separate individualbiochemical component eigenspectra. In previousstudies [1–8] the band peaks at 1,650 and 1,549cm−1, have been associated with peptide Amide Iand Amide II vibrations, and the peak at 1,743cm−1 has been attributed to C4O stretchingmodes of lipids. Based on these studies, we there-fore preliminarily assign eigenspectra A and B ofnormal skin tissue to peptides and lipids, respec-tively. The detailed vibrational band assignmentsare given by Brooks et al. [1]. The explicit identi-fication of the biochemical origins of theeigenspectra isolated in this study still remains tobe performed. A visual inspection of the eigen-spectra plots reveals that they are unique for eachdisease state, hence knowledge of the specific bio-chemical identities and how they vary with dis-ease states would be indispensable diagnostic in-formation.

In Figure 7 we have shown principal factorcluster plots of different skin states, namely mela-noma and basaloma tumors, and normal skin tis-sue. Specifically, principal biochemical factor 2 isplotted vs. principal biochemical factor 1. Theclusters in Figure 7 have been confined into intu-itively drawn enclosures. It can be seen that mela-noma and normal clusters are well localized andseparated from each other hence this makes clus-ter analysis a potentially suitable method formelanoma tumor and normal skin classificationor diagnosis. The proximity of the points withineach cluster, melanoma tumors and normal skintissue, seems to imply biochemical similarity orhomogeneity of the respective skin states. The ba-saloma points are not well localized hence exhibitsubstantial variability in their biochemical char-acteristics.

TABLE 1. Eigenvalues for Each Skin State Spectrum

ClassificationFactor

Number Eigenvalue Eigenvalue (%)

Normal 1 (A) 548.42 99.442 (B) 2.57 0.473 0.24 0.044 0.14 0.035 0.08 0.016 0.05 0.01

Basaloma 1 (A) 462.14 93.232 (B) 25.75 5.203 4.20 .854 2.03 0.415 1.31 0.266 0.28 0.06

Melanoma 1 (A) 433.74 99.172 (B) 2.88 0.663 0.45 0.104 0.18 0.045 0.11 0.026 0.01 0.00

Factor Analysis of Cancer FTIR-FEW Spectra 385

This study reveals the potential of chemicalfactor analysis in medical diagnostics. The iso-lated eigenspectra are unique for each skin statehence can be used as disease markers. The clusterplots for normal skin tissue and melanoma tu-mors are well discriminated and this is in agree-ment with pathological preclassifications. To im-

prove the chemical factor analysis method resultswe plan to use a larger number of subjects. Inaddition several parameters will be included suchas sex, age, diet, weight index of specific patientsor subjects. Such parameters would shed morelight on the development of skin cancer and con-tributing factors.

Fig. 3. In vivo measurements of FourierTransform Infrared Fiberoptical Eva-nescent Wave (FTIR-FEW) spectra fornormal human skin tissue in the range1480–1800cm−1. (a) Experimental rawdata, (b) factor analyzed model spectrashowing two isolated components A andB based on the two optimal eigenvalues.C represents the sum of spectra Aand B.

Fig. 4. In vivo measurements of FourierTransform Infrared Fiberoptical Eva-nescent Wave (FTIR-FEW) spectra forhuman skin tissue for basaloma in therange 1480–1800cm−1. (a) Experimen-tal raw data, (b) factor analyzed modelspectra showing two isolated compo-nents A and B based on the two optimaleigenvalues. C represents the sum ofspectra A and B.

386 Sukuta and Bruch

CONCLUSIONThis study revealed that chemical factor

analysis can be used to analyze complex humanskin in vivo FTIR-FEW spectral data and availthe results in compact and intuitive ways. Theperformed analysis was carried out directly onraw data and is independent of specific spectro-scopic parameters such as band peaks, positions,peak shifts, peak heights, and intensity ratios.

The method was able to compress or reduce ourFTIR-FEW skin spectral data spanning 320 vari-able wavenumbers to only two principal biochemi-cal factors. The two new variables or principalbiochemical factors, for each skin state, have beenused for the extraction of biochemical componenteigenspectra and classification.

The next step in our analysis will be theformal identification of isolated component eigen-

Fig. 5. In vivo measurements of FourierTransform Infrared Fiberoptical Eva-nescent Wave (FTIR-FEW) spectra forhuman skin tissue for melanoma in therange 1480–1800cm−1. (a) Experimen-tal raw data, (b) factor analyzed modelspectra showing two isolated compo-nents A and B based on the two optimaleigenvalues. C represents the sum ofspectra A and B.

Fig. 6. Comparison of characteristicmiddle infrared spectra for (a) normalskin, (b) basaloma and (c) melanoma. Aand B are the isolated component spec-tra due to the two largets eigenvectors(see Table 1). Also shown is spectrum C,the sum spectrum of components Aand B.

Factor Analysis of Cancer FTIR-FEW Spectra 387

spectra A and B biochemical origins. This theoret-ical study will provide more insight on the ambi-ent biochemical environment in normal skin, ba-saloma and melanoma tumors.

ACKNOWLEDGMENTS

This article was presented at the 18th An-nual Meeting of the American Society of LaserSurgery and Medicine in April 1998, San DiegoCalifornia. Sydney Sukuta is grateful to the Soci-ety for the generous travel grant to attend andpresent the study. In addition the authors are in-debted to Dr. Natalia Afanasyeva and SergeiKolyalov from the Institute of Spectroscopy, Rus-sian Academy of Sciences for providing the dataand fruitful discussions.

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Fig. 7. Two dimensional plot of princi-pal biochemical factors for differentskin states that include normal skin,basaloma and melanoma tumors.

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