identification of tumor tissue by ftir spectroscopy in combination with positron emission tomography
TRANSCRIPT
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: [email protected] (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.
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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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