Identification of tumor tissue by FTIR spectroscopyin combination with positron emission tomography

Tom Richtera, Gerald Steinera, Mario H. Abu-Ida, Reiner Salzera,*,Ralf Bergmannb, Heike Rodigb, Bernd Johannsenb

aInstitute of Analytical Chemistry, Dresden University of Technology, D-01062 Dresden, GermanybInstitute of Bioinorganic and Radiopharmaceutical Chemistry, Research Center Rossendorf, D-01314 Dresden, Germany

Received 17 August 2000; received in revised form 12 May 2001; accepted 14 May 2001

Abstract

A method is described for identifying tumor tissue by means of FTIR microspectroscopy and positron emission tomography

(PET). Thin tissue sections of human squamous carcinoma from hypopharynx (FaDu) and human colon adenocarcinoma

(HT-29) grown in nude mice were investigated. FTIR spectroscopic maps of the thin tissue sections were generated and evaluated

by Fuzzy C-Means (FCM) clustering and principal component analysis (PCA). The processed data were reassembled into images

and compared to stained tissue samples and to PET. Tumor tissue could successfully be identified by this FTIR microspectroscopic

method, while it was not possible to accomplish this with PETalone. On the other hand, PET permitted the non-invasive screening

for suspicious tissue inside the body, which could not be achieved by FTIR. # 2002 Elsevier Science B.V. All rights reserved.

Keywords: FTIR spectroscopy; Positron emission tomography; PET; Autoradiography; Tumor; PCA; FCM

1. Introduction

Tumor cells show changes in their internal bio-

chemical processes compared to normal cell tissue.

Such changes arise long before the cells can histolo-

gically be identified as abnormal. A promising answer

to the demand of improving the diagnostic capabilities

consists in the combination of different bioanalytical

methods. One of the most promising combinations is

positron emission tomography (PET) with FTIR spec-

troscopy. The high potential of PET [1] and FTIR

spectroscopy [2,3] for separately diagnosing cancer

has already been demonstrated. FTIR imaging with

synchrotron radiation has successfully been used to

study different types of smallest tissue samples with

a spatial resolution down to 6 mm [4]. The spatial

resolution achieved by PET is presently restricted to

several millimeters.

At present the most common way of identifying

tumor tissue is the evaluation of a stained thin section

of tissue by an experienced pathologist. A great

number of different staining techniques exists. The

selection of the procedure most appropriate to solve

a special problem depends on many factors. We used

the staining according to Papanicolaou [5] because

all important cell components are emphasized: cell

nuclei, connective tissue, and cytoplasm.

FTIR spectroscopy offers the capability of identi-

fying biochemical substances because of the highly

distinctive features of the characteristic molecular

Vibrational Spectroscopy 28 (2002) 103–110

* Corresponding author. Tel.: þ49-351-463-2631;

fax: þ49-351-463-7188.

E-mail address: reiner.salzer@chemie.tu-dresden.de (R. Salzer).

0924-2031/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 9 2 4 - 2 0 3 1 ( 0 1 ) 0 0 1 4 9 - 7

vibrations rendered in the spectra. A vast amount

of substances exists within the cells, and most of

those substances contribute specifically to the vibra-

tional spectra. Changes in the composition of the cells’

biochemistry should therefore be detectable by FTIR

spectroscopic investigation. Although it is most likely

that the variety of all those changes appear ambigu-

ously in the spectra, multivariate data evaluation tools

should provide access to the diagnostic features

hidden among the abundance of spectroscopic infor-

mation. A serious limitation of FTIR spectroscopy

in the mid infrared range (400–4.000 cm�1, 25.000–

2.500 nm), which carries most of the specific mole-

cular information, is the very low penetration depth in

biological tissue. The low penetration depths prevents

mid infrared radiation from being utilized for tomo-

graphy of inner parts of the body. However, in vivo

spectroscopic measurements on inner parts of the body

should be feasible by using mid infrared fibers [6]

during or shortly after a PET examination, provided

the above mentioned information can be extracted

from the spectra [7].

PET is the best scintigraphic method presently

available for the in vivo allocation of enhanced meta-

bolic activities across the body. PET is based on the

detection of positrons emitted upon the decay of

particular nuclides like 18F, 15O, 13N, or 99mTc.99mTc (radioactive half-life of 6 h) is a metastable

daughter nuclide of 99Tc (radioactive half-life of

210.000 years). Its short half-life makes 99mTc a

convenient radioactive tracer element. All nuclides

mentioned above are chemically bound to special

compounds in order to transport or to accumulate

them at particular locations inside the body. The

emitted high energetic radiation (511 keV) is observed

by an assembly of detectors arranged annularly around

the object under investigation. A three-dimensional

map of the concentration of the marker compounds

across the object is obtainable by appropriate combi-

nation of the detector signals.

One of the most important compounds for clinical

application of PET is 2-[99mTc]fluoro-2-deoxy-D-glu-

cose, abbreviated [18F]FDG or FDG. FDG is incor-

porated like non-derivatized glucose, but in contrast to

the latter FDG is not metabolized. For this reason,

FDG accumulates in the cells over time. Another

common tracer, [99mTc]sestamibi, can be con-

sidered analogous to a potassium ion, therefore, it

accumulates in cells with high metabolic activity, too.

The obtained PET images provide information about

regions of enhanced metabolism. Such enhanced

metabolism is indicative of cancer, but of a number

of common irritations as well. The lack of differentia-

tion between tumor and harmless irritation is a serious

drawback in PET.

Because of the above mentioned facts, a combina-

tion of infrared spectroscopy and PET should result in

a powerful tool for obtaining information of improved

diagnostic value for tissue differentiation. In this

report, we present our current results in generating

unambiguous FTIR maps and their combination with

PET data.

2. Experimental

The experiments were carried out on animal tumor

models from nude mice in conformity with the rele-

vant national regulations. 5 MBq of the lipophilic99mTc cation hexakis(2-methoxy-isobutyl-isonitrile)-

technetium(I), ([99mTc]sestamibi; Du Pont Pharma

GmbH, Bad Homburg, Germany) or 2 MBq of 2-

[18F]fluoro-2-deoxy-D-glucose ([18F]FDG; Dip. PET

Chemie, FZR Rossendorf, Germany) was injected into

the tail veins of 25 g athymic nude mice. The injected

amount of radioactive tracer is usually given in MBq

(megabecquerel), which relates most directly to its

activity. In our case, the injected amount of tracer is in

the range of fmol. All mice were bearing a solid tumor

on one rear leg from human squamous carcinoma from

hypopharynx (FaDu) or from human colon adenocar-

cinoma cells (HT-29). All tumors were <10 mm in

diameter.A total of 30 min post injection the mice

were heart punctured under ether anesthesia. Selected

organs were isolated for weighing and positron count-

ing. The samples were assayed for gamma activity in a

multichannel well-type sodium iodine gamma counter

(COBRA II, Packard Instrument Company, Meriden,

CT, USA) using two energy windows (99mTc: 110–

180 keV and 18F: 450–1500 keV). Additional to the in

vivo PET images we used thin sections of the tumor

to obtain autoradiographic images, which offer a

resolution (approximately 100 mm) superior to the

PET images (approximately 10 mm). For autoradio-

graphy and infrared spectroscopy, a 10 mm thick tumor

section was cut from the frozen tissue block (without

104 T. Richter et al. / Vibrational Spectroscopy 28 (2002) 103–110

freezing medium) by a cryocut (Leica CM 1805,

Bensheim, Germany). The sections were transferred

onto 76 mm � 26 mm � 2 mm CaF2 windows and air

dried. Each window with sample was fixed to an

imaging plate (BAS MP 2040, Raytest, Straubenhardt,

Germany) and exposed for 30 min. Subsequently, the

imaging plate was scanned using a bio-imaging

analyzer (BAS 2000, FUJI Photo Film Co., Tokyo,

Japan). The obtained image was evaluated with the

software AIDA (Version 2.11, Raytest, Straubenhardt,

Germany). In this paper only one thin section of a

HT29 tumor is shown as an example. Fig. 1 shows an

autoradiographic image of that sample marked with

FDG.

A Nicolet 5PC FTIR spectrometer (Nicolet, Offen-

bach, Germany) equipped with an IR microscope (IR-

Plan, SpectraTec Ltd., Brichwood, UK) and MCT

detector was used to record the infrared spectra. A

total of 64 interferograms for the spectral range 1800–

950 cm�1 were co-added at 4 cm�1 resolution and

zerofill factor 1. A rectangular knife-edge aperture

was used to select a 90 mm � 90 mm area of the

sample. Infrared maps were generated using a com-

puterized XY stage. The measuring grid in this parti-

cular case consisted of 44 � 53 rectangles, i.e. an

array of 2332 spectra was obtained for a sample

area of approximately 4 mm � 5 mm. Both the stage

and the spectrometer were controlled by a home-

made Visual Basic (Version 6.0, Microsoft Corp.) pro-

gram together with the Omnic software (Version 4.01,

Nicolet Instrument Corp.). The stage was shifted

in increments of 90 mm in both X and Y directions.

At first a background spectrum was recorded from

a sample-free area of the CaF2 window. After every

10 sample spectra a new background spectrum was

recorded at the same position as the first one.

For histological characterization, the same section

of the sample was stained after autoradiography and

IR mapping. For staining, Papanicolaous solutions

1a, 2a and 3a (Merck, Darmstadt, Germany) were

used [5]. After staining the sample was dehydrated

in an ethanolic Rotihistol solution (Roth, Karlsruhe,

Germany), and the color of the cell nucleus turned into

blue, the cytoplasm of acidophilic cells into pink or

red coloring, and the cytoplasm of basophilic cells into

a blue–green one (Fig. 2).

3. Results

The PET image (Fig. 3) easily allows for the tumor

detection within the mouse. This image was recorded

from another mouse because it is not possible to obtain

both the PET image and the autoradiogram at the same

time. In the case depicted in Fig. 3, [99mTc]sestamibi

was used as radioactive tracer. As mentioned above,

all regions of enhanced metabolism appear as bright

spots in the image. Therefore, all inner organs are

looming. The tumor is also well visible (arrow in

Fig. 3). Due to the blood–brain barrier no activity

was found in the brain. In addition to the tumor,

enhanced positron emission is also found in Fig. 3

for healthy organs of higher metabolic activity. It

means that spots of enhanced metabolic activity are

Fig. 1. Autoradiogram of a tumor thin section. Light color

indicates areas of enhanced radioactivity.

Fig. 2. Thin section of Fig. 1 after Papanicolaou staining.

T. Richter et al. / Vibrational Spectroscopy 28 (2002) 103–110 105

easily located by PET, whereas tumors and other tissue

of higher metabolic activity (e.g. due to a current

inflammation) can not be distinguished from each

other solely on the basis of PET data.

The acquired FTIR spectra were at first tested for

validity. Bad spectra were sorted out. This was done by

an automatic filter routine written in MatLab (Version

5.3, MathWorks Inc., Natric, MA, USA). The filter

criteria applied during the test comprise the intensity

of amide I band, strong baseline drifts, noise, and

indicators of freezing medium, just to make sure that

no freezing medium was used. Only spectra which

passed the tests were retained for further calculations.

All valid spectra were offset corrected. Fig. 4 shows

the microscopic VIS picture of the sample (assembled

from 30 sequential frames) with the measuring grid

overlaid. Three typical infrared spectra of the essential

histological regions are also displayed. Apparently,

the spectra are very similar except for some overall

changes in intensity and some minor changes at

locations in the range <1300 cm�1.

The spectral range used for evaluation was limited

to 1480–950 cm�1, which only includes the main vibra-

tional features of nPO2(1250–1220 and 1080 cm�1),

nC–N, amide III (1310–1240 cm�1) and dCH2/CH3

(1468 and 1380 cm�1, respectively). In this spectral

range, the most important changes between tissue

spectra are expected. The dominant amid I and amid

II bands (around 1656 and 1543 cm�1, respectively)

were excluded to ensure the following calculations

are not effected by these less decisive bands. Subse-

quently, the spectra were normalized to a standard area

below the curve in order to minimize unpreventable

Fig. 3. PET image (coronal) of the mouse. Areas of high activity

appear as bright spots. The tumor is marked with a white arrow.

Fig. 4. VIS image of the sample pooled from 30 microscopic pictures. A measuring grid was overlaid and infrared spectra from three

representative regions were selected: (a) connective tissue, (b) growing tumor and (c) necrotic tumor. The spectra are shifted along the y-axis.

106 T. Richter et al. / Vibrational Spectroscopy 28 (2002) 103–110

errors due to unequal shrinking during the drying

process of the sample.

Principal component analysis (PCA) [8] and Fuzzy

C-Means (FCM) [9] clustering were used to identify

different tissue types. By PCA calculation, we extracted

the first 20 principal components of the original data

set which are orthogonal to each other. Fig. 5 shows

the first four principal components which explain over

Fig. 5. Score maps (left) and loading plots (right) of the first four principal components. Areas of high values in the score maps appear bright.

T. Richter et al. / Vibrational Spectroscopy 28 (2002) 103–110 107

99% of the total variance of the dataset. The score maps

are displayed on the left panel, the corresponding

loading plots on the right panel. The loading plots

indicate, which spectral feature was chosen by purely

mathematical reasons for the rating in the score map of

each particular principal component (PC). Loading

plots are important links between the mathematical

procedure and the spectroscopic background. The influ-

ence of mean centering on the results of the PCA was

checked for the current data. It turned out to be of

no influence except that one more PC has to be taken

into consideration. For this reason no mean centering of

the data was done, i.e. the first PC comprises the most

common variance within all spectra and the loading plot

for that PC represents the mean spectrum of the whole

tissue sample. The loading plots of the next principal

components reveal more subtle features of the spectra,

mainly in the regions around 1100 and 1025 cm�1.

The FCM algorithm was originally introduced as an

improved clustering method. It provides for less rigid

decisions during the mathematical evaluation process.

The procedure we used (MatLab Fuzzy Logic Toolbox

Version 2.0.1) was set to find six clusters. The centers of

the six calculated clusters are shown in Fig. 6. The most

apparent differences occur again in the region between

1100 and 1000 cm�1. In this example, each measured

spectrum was assigned to one calculated cluster acco-

rding to it’s highest membership grade. All assigned

clusters were color-coded and superimposed on the

microscopic picture (Fig. 7, right). The colors used

correspond to the colors of the cluster centers in Fig. 6.

To improve the interpretability of the PCA score

maps in Fig. 5, the scores of the second, third and fourth

principal component were combined to form one RGB

image (one PC in each color channel). The RGB image

was superimposed to the microscopic picture (Fig. 7,

Fig. 6. Cluster centers of the FCM calculation (plots are shifted along the y-axis).

108 T. Richter et al. / Vibrational Spectroscopy 28 (2002) 103–110

Fig. 7. IR spectroscopic images assembled with the VIS microscopic picture: (left) RGB image of the second, third and fourth PC of the IR spectra; (center) VIS picture of the

stained sample with overlaid measuring grid; (right) image of the FCM clusters of the IR spectra.

T.

Rich

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left). For comparison, the picture of the stained sample

with the measuring grid (Fig. 7, center) is displayed,

too. Every detail seen in the stained sample can also be

identified in the spectroscopic images (Fig. 7, left and

right). The RGB image of the PCA calculation seems to

reveal more details than the picture of the stained

sample. The histological significance of these addi-

tional details still has to be verified.

4. Conclusion

Compared to the autoradiogram (Fig. 1), the FTIR

mapping provides a substantial improvement in dis-

criminating tumor from healthy tissue. Both FCM

clustering and PCA are well suited to distinguish

different types of tissue based on their FTIR maps.

Despite the differences in the mathematical back-

ground of FCM and PCA, both independent algo-

rithms calculated comparable results, with the FCM

algorithm providing a more general view on the main

features of the tumor thin section. This points to the

validity of the results obtained by the chosen proce-

dures. Further efforts will be made to extent these

results in order to classify spectra of tissue samples.

Especially the PCA calculation offers the possibility

of building up a SIMCA classification algorithm [10]

for tumor diagnosis.

Acknowledgements

The financial support by the Sachsisches Staatsmi-

nisterium fur Wissenschaft und Kunst (SMWK) for this

project is gratefully acknowledged.

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