Near infrared light interaction with lung cancer cells

Abstract

The objective of this study is to explore the phenomenology of near infrared (NIR) light interaction with healthy and early-lung cancer by combining efficient polarimetric backscattering detection techniques with Polarimetric Exploratory Data Analysis (pEDA). Preliminary results indicate that enhanced discrimination signatures can be obtained for certain types of lung cancers.

Full text

Near Infrared Light Interaction with Lung Cancer
Cells
G.C. Giakos [1]-[2], S. Marotta [2], C. Narayan [2], J.
Petermann [1], S. Sestra, D. Pingili [1], S. A.
Tsokaktsidis [1], D.B. Sheffer [1], and W. Xu [1]
[1] Department of Electrical and Computer
Engineering
[2] Department of Biomedical Engineering
The University of Akron
Akron, Ohio 44325
USA
e-mail:giakos@uakron.edu
M. Zervakis [3], G. Livanos [3], M. Kounelakis [3]
Department of Electronic and Computer Engineering
Technical University of Crete
Chania 73100, Greece
e-mail: michalis@display.tuc.gr
Abstract— The objective of this study is to explore the
phenomenology of near infrared (NIR) light interaction with
healthy and early-lung cancer by combining efficient
polarimetric backscattering detection techniques with
Polarimetric Exploratory Data Analysis (pEDA). Preliminary
results indicate that enhanced discrimination signatures can be
obtained for certain types of lung cancers.
Keywords-lung cancer; early detection and enhanced
discrimination; physical phenomenology of polarized light with
cancer tissue pathologies; Polarimetric Exploratory Data Analysis
(pEDA)
I. INTRODUCTION
The purpose of this study is to develop efficient and reliable
techniques that would lead to an early identification and
discrimination of precancerous and cancerous lung pathologies
so that to lead to accurate diagnosis and efficient treatment of
lung cancer.
Early detection of lung cancer is of paramount
significance. Latest multi-year trials revealed that low-dose
spiral computed tomography (CT) can be promising modality
for lung cancer screening. However, spiral CT is limited for
detecting small peripheral lesions. On the other hand, heavy
smokers develop tumors located in the central airways; as
result, other techniques besides CT are needed for early
detection. For instance, squamous cell carcinoma of the central
airway is thought as a multistep process starting from a
squamous metaplasia which progresses to dysplasia, followed
by carcinoma in situ (CIS), finally, progressing to invasive
cancer [1]-[4].
Central tumors are generally squamous cell carcinomas, while
most peripheral tumors are adenocarcinomas or large cell
carcinomas are peripherally located. Because of their
peripheral location, adenocarcinomas may not call attention to
themselves until they have developed extrathoracic
metastases. For example, patients may present with clinical
signs of bone spread or intracranial metastatic disease.
1) Non-small cell lung cancer (NSCLC)
NSCLC accounts for about 80% of lung cancers. There are
different types of NSCLC, including:
1) Adenocarcinoma. This is the most common type of
NSCLC (about 40%). This cancer is comprised of
cells that excrete mucus and occurs mostly at the
periphery of the lung. It can be divided into four
categories:
xAcinar
xBronchoalveolar
xMucus-secreting
xPapillary
Because of their peripheral location, adenocarcinomas may
stay unobserved until they have developed extrathoracic
metastases.
1. Squamous cell carcinoma. This is the second most
common type of NSCLC. It forms in the trachea and
bronchi and exhibits a remarkable dose-dependence
with cigarette smoking.
2. Large-cell carcinoma (about 10% of all lung
cancers). This cancer grows rapidly near the surface,
or outer edges, of the lungs.
2) Small cell lung cancer (SCLC)
SCLC accounts for about 10-15% of all lung cancers. They
usually grow on the bronchial tree, but they mostly stay on the
lung side of the bronchus rather than growing in the airway.
Although the cells are small, they multiply quickly and form
large tumors that can spread throughout the body. Smoking is
almost always the cause of SCLC. Due to the rapid spread of
978-1-4244-7935-1/11/$26.00 ©2011 IEEE

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NormalizedI ntensities (V)
a
blished me
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uction Tech
p
ective ideal
m
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erimentally o
b
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et, in this c
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ile the experi
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4 Comparison of
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enerator retarder
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eriment, thre
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mal lung tissu
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, soli
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nch and a po
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erating branc
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ar polarizer P
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), so that th
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t
ial polarizatio
n
020 40
0.2
0.3
0.4
0.5
0.6
0.7
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1
G
e
NormalizedI ntensities (V)
Theor eti
c
t
hodologies,
d
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nique” [5],
m
atrices. As
a
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se a linear h
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the present st
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a
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trix of LHP
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ar results.
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trix is,
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during calibratio
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nearly Polari
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Fig. 5. In t
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n
sm
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e
r pulses were
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ated a lung c
en backscatte
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ing of a qua
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ircularly pol
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sion with ref
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EXPERIME
N
n
g the exp
e
a
ttered signa
c
erous tissue
(
were obtaine
d
t
ry.
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stological d
e
e
d in these exp
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n
g Carcinoma
a
ll cell lung
c
u
amous featur
e
c
ified, fib
r
r
ring. Resecti
o
u
carcinoma.
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l
transmitted th
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ancer tissue
g
r
ed in the dir
e
r
te
r
-wave ret
a
polarizer P2
(
w
Focus 2151
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s 1 mm diame
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A
/W. Optical
m
e
ar (co-
p
olar
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c
onfiguration.
histograms
w
Wave Anal
y
s
ubroutines. I
n
a
rized waves
a
e
rence to Figs.
N
TAL
RESUL
T
e
rimental arr
a
l contributio
n
(
carcinoma n
s
d
under copo
e
scription of
e
riments is the
In Situ (CIS):
c
arcinoma(NS
C
e
s, apparently
r
otic nodu
l
o
n margin de
m
l
arrangement
r
ough the gen
e
g
lass slide arr
a
e
ction of the
a
a
rder R
2
, aga
i
(
parallel to P
1
femtowatt p
ter aperture a
n
r
(NEP) 15
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11
, an
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m
easurements
w
i
zed) and c
r
The acquire
d
w
ere then reco
y
zer, then pro
c
n
deed, preli
m
a
re described
a
10-11.
T
S
AND
DISC
U
a
ngement of
n
s from he
a
s
itu) and stag
e
larized and c
r
t
he lung ca
n
following:
Poorly diffe
r
C
LC), with
g
arising in ass
o
l
es and
m
onstrates pa
t
e
rator system
a
y. The light
a
nalyzer arm
i
n set at 0
0
,
1
) which was
hotodetector.
n
d exhibits an
fW/Hz
1/2
, a
d
a typical
w
ere obtained
r
osspolarized
d
waveforms
rded using a
c
essed using
m
inary results
a
t the end of
U
SSION
Figure 5,
a
lthy tissue,
e
I cancerous
r
osspolarized
n
cer samples
r
entiated non
g
landular and
o
ciation with
sub-plueral
t
chy, focal in

B. Stage I Lung Carcinoma: Non-small cell lung carcinoma
(NSCLC)-adenocarcinoma with papillary
bronchioloalveolar pattern, T1, N0, G2.
Both types of cancers are not invasive.
At first place, repeatability experiments took place
aimed at assessing the stability of the polarimetric system.
In Fig. 6 and 7, repeated measurements of detected
amplitudes of backscattered signal contributions under
copolarized and crosspolarized geometries are reported. A
comparison between mean backscattered signal amplitudes
for both geometries is shown in Fig. 8. In Fig. 9, the degree
of linear polarization (DOLP) between normal lung tissue,
stage I lung carcinoma, and glass (reference material) is
shown. The DOLP has been estimated based on Eq. 8
where the tissue contributions are normalized with respect
to the glass.
Fig. 6 Normal versus stage I Lung Tissue
Amplitude (under copolarized geometry)
Fig. 7 Normal versus Stage I Carcinoma Lung Tissue
Amplitude (under crosspolarized Geometry)
Fig. 8 Mean amplitude comparison among different tissue pathologies
(collinearly polarized light)
Fig. 9 Degree of Linear Polarization (DOLP) comparison between stage I and
normal lung cancer tissue
(8)
By applying the Mueller matrix decomposition
technique described in Section II.A, the intensities of a total of
sixteen 1-d Mueller matrix elements imaging signals for
different samples are shown.
Fig. 10 Depolarization Mueller matrix elements for different lung tissue
pathologies
0
50
100
150
200
250
300
350
400
12345
DetectedAmplitude(mV)
MeasurementNumber
Normal Mean
Amp.
StageI Mean
Amp.
Glass Mean
Amp
0
10
20
30
40
50
60
70
123
DetectedAmplitude(mV)
MeasurementNumber
Normal Mean
Amp.
StageI Mean
Amp.
Glass Mean
Amp.
0
100
200
300
400
Copolarized Crosspolarized
MeanAmplitude
(mV)
Normal
StageI
Glass
0.0000
0.2000
0.4000
0.6000
Normal StageI
DOLP
0.4
0.2
0
0.2
0.4
0.6
0.8
1
1.2
m11
m12
m13
m14
m21
m22
m23
m24
m31
m32
m33
m34
m41
m42
m43
m44
NormalLung CarcinomaInSitu
StageICarcinoma
glass
sample
glassII
sampleII
glass
sample
glassII
sampleII
I
I
I
I
I
I
I
I
DOLP
_
_
_
_
_
_
_
_
A
A
A
A



Following the treatment of Section 2.B the dynamic range of
histograms fitted on a normal distribution are plotted for
different samples is shown in Fig. 11.
Fig. 11 Dynamic range of histograms fitted to Gaussians for different lung
tissue pathologies (glass serves as reference)
The experimental findings of this study supports the
observation trends of other studies on different types of
cancer, like oral and colon cancers, that early cancerous
lesions depolarize light less than healthy tissues [14]-[15].
This statement should be applied with cautiousness as
applicable only for certain types of cancers, because of the
diverse histological, morphological and molecular information
exhibited by different cancer types. The depolarization
Mueller matrix elements for different lung cancer tissue
pathologies indicate that enhanced discrimination among
different lung cancer is obtained through circularly type
polarized waves rather than linearly polarized waves; these
experimental data are supported by sound statistics as shown
in a companion paper. The dynamic range metrics is
proportional to the degree of polarization of light.
V. CONCLUSION
The phenomenology of light interaction with lung cancer
cells was presented. The outcome of this preliminary study
highlights the importance of polarized light interrogation of
lung cancer tissues as an indispensible diagnostic tool aimed at
enhancing the early detection and discrimination potential
among different lung cancer types. Further research is in
process in order to assess, identify, and classify different types
of early lung cancers with high accuracy, specificity, and low-
false alarm rate.
VI. REFERENCES
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Perelman, M.S. Feld, “Polarized light scattering spectroscopy for quantitative
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Selected Topics in Quantum Electronics, vol. 5 Issue, 4, 1019 – 1026, 2002.
[2] K. Sokolov, R. Drezek, K. Gossage, and R. Richards-Kortum,
"Reflectance spectroscopy with polarized light: is it sensitive to cellular and
nuclear morphology," Opt. Express 5, 302-317 (1999).
[3] J. R. Mourant, A. H. Hielscher Ph.D., A. A. Eick B.S., T. M. Johnson, J. P.
Freyer , “Evidence of intrinsic differences in the light scattering properties of
tumorigenic and nontumorigenic cells”, Cancer Cytopathology, Vol. 84, Issue
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[4] A. Gakuin, “Discrimination analysis of human lung cancer cells associated
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[5] R.A. Chipman, Polarimetry Handbook of Optics. 2
nd
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[6] G. C. Giakos, Multifusion Multispectral Lightwave Polarimetric Detection
Principles and Systems, IEEE Transactions on Instrumentation and
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[7] R.M.A. Azzam, “Photopolarimetric measurement of the Mueller matrix by
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[8] D. H. Goldstein, “Mueller matrix dual-rotating retarder polarimeter,”
Applied Optics, 31, 31, pp 6676-6683, 1992.
[9] M.H. Smith, “Optimization of a dual-rotating–retarder Mueller matrix
Polarimeter”, Applied Optics, Vol. 41, No. 13, May 2002.
[10] D. B. Chenault, J. L. Pezzaniti, and R. A. Chipman, “Mueller matrix
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1992.
[11] G. C. Giakos, “Novel Biological Metamaterials, Nanoscale Optical
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and Imaging Systems Engineering, Vol 3, No 1, pp 3-12, 2010.
[12] G.C. Giakos, K. Valluru, K. Ambadipudi, S. Paturi, P. Bathini, M.
Becker, P. Farajipour, S. Marotta, J. Paxitzis, B. Mandadi , Stokes Parameters
Imaging of Multi-index of Refraction Biological Phantoms utilizing Optically
Active Molecular Contrast Agents, Measurement Science and Technology,
Institute of Physics (IOP), vol. 20, 2209, pp. 1-12, September 2009.
[13] S. Lu and R. A. Chipman, “Interpretation of Mueller matrices
based on polar decomposition,” J. Opt. Soc. Am. A 13, 1106–1113, 1996.
[14] Antonelli M.-R., Pierangelo, A., Novikova, T., Validire, P., Benali, A.,
Gayet, B., et al., “Mueller matrix imaging of human colon tissue for
cancer diagnostics: how Monte Carlo modeling can help in the interpretation
of experimental data”, Optics express, vol. 18(10), pp. 10200-8, 2010
[15] J. Chung, W. Jung, M.J. Hammer-Wilson, P. Wilder-Smith, and Z. Chen,
“Use of polar decomposition for the diagnosis of oral precancer”, Applied
Optics, vol. 46(15), pp. 3038-45, 2007.
0.1000
0.1200
0.1400
0.1600
0.1800
0.2000
Glass Normal InSitu StageI
D.R.(dB)