1. health and fitness
2. disease
3. cancer

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Potential of Urine Dielectric Properties in
Classification of Stages of Breast Carcinomas
Ausilah Al-Fraihat1,a
A. Wesam Al-Mufti2, a, U. Hashim3, b, and Tijjani
Adam2,c
The Hashemite University
P.O. Box 150459, Zarqa 13115, Jordan
osaila2005@yahoo.com
Nano structure lab on chip research group
Institute Nano Electronic Engineering, Universiti Malaysia
Perlis. (UniMAP), 01000 Kangar, Perlis Malaysia
a
mohamenw@gmail.com, b uda@unimap.edu.my
Abstract--Some evidences have been found that urine can be
used as a biomarker for the detection of many types of diseases.
The purpose of this study is to use permittivity of urine to assess
whether different stages of breast cancer are significantly
different. Changes in electrical resistance of urine samples to
applied microwaves were measured over a frequency range
between 10MHz to 20GHz. This is carried out by the
measurement of samples’ response to applied microwave energy
using network vector analyzer. SPSS was used to find out the
significant difference in urine permittivity values across different
stages of breast cancer. The results show significant difference
between stage 1 and stage 2 in all dielectric parameters of urine
permittivity. Significant difference was also found between stage
2 and stage 3 in both dielectric constant and loss factor
parameters while the significant difference between stage 1 and
stage 3 only exists in loss factor parameter. The results suggest
that it is possible to estimate the stage of breast cancer based on
the dielectric properties of urine.
Interest and research in studying the electrical properties of
breast tumors began several decades ago. All of these studies
are based on the consensus that electrical properties of breast
tumors are different than that for healthy tissues. Fricke and
Morse have studied the electrical characteristics of malignant
breast tumors. They observed higher permittivity values of
tumor tissues at 20 KHz in comparison with normal tissues
[4]. Roberts et al. found out an increment of the loss factor
and the dielectric constant of normal breast tissue and fatty
mammary tissues fibrosed by x-rays [5]. Same observation
has been reported by Singh et al [6].
In 1988 Surowiec and colleagues carried out in vitro tests to
find out the differences of characteristics between samples of
breast cancer, samples of cancer and normal tissue combined
with the boundary of the lesion, and samples of normal tissue
only on frequency range between 20 kHz to 100 MHz. They
found out that the conductivity and the dielectric constants of
tissues of carcinoma are different across the sample groups
[7].
Keywords: Permittivity, Dielectric Properties, Microwave
Frequency, Tumor, Biomarker, Stages of Breast Cancer
I.
In this paper, a new method is employed to investigate
whether there is a significant difference across three groups of
breast cancer stages (stage 1, stage 2 and stage 3). This by
measuring permittivity value of their urine samples as a
function of microwave frequency. Existence of significant
difference within different stages of breast cancer indicates
the effect of cancer tumor progress on urine properties and
possibility of using urine for breast cancer diagnostic
purposes. Changes in the dielectric properties of urine reflect
the progress of malignant tumor.
INTRODUCTION
A malignant tumor is a mass of cells that grow abnormally
and invade surrounding tissues or invade different regions of
the body. Malignant tumor which metastasizes from the breast
cells is called breast cancer. Breast cancer is considered as the
most common type of cancer in women (excluding skin
cancers) [1-2]. Staging and Early detection of breast cancer is
a key factor in prognosis, and consequently plays a major role
in reducing mortality [3]. Staging of breast cancer is currently
performed by information that is attained from the pathology
report which is based upon detections by imaging exams, such
as chest x-ray, abdominal ultrasound and computed
tomography (CT or CAT scan, and bone scans). 20% failure
of X-ray mammography to detect malignant tumors and
limitations of other current methods such as ultrasound and
MRI motivate scientist to seek for complimentary techniques
to overcome these limitations.
II.
METHODS
In this study, urine samples were collected from 19 breast
cancer patients (stage 1: 6, stage 2: 7, stage 3: 6, women, age:
53.52 years old). Malaysians, Chinese and Indians, all from
Malaysia were recruited in this study. The study was approved
by university of Malaya medical center. Informed consent was
obtained from all of the participants. Table 1 includes
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In order to expect the relation between the stages before
performing the statistical analysis, graphs of the permittivity
of the subjects with different stages were plotted in both real
part and imaginary part, Figures (1-2). Due to the overlapping
of the graphs two subjects from each stage were chosen to
depict the trend of the graphs. Data, obtained from the
network vector analyzer, is analyzed statistically over
frequency range from 4.1E+8 to 2E+10 Hz through the use of
statistical analysis software, SPSS. Statistical analysis method
applied by SPSS is One Way ANOVA test followed by
Turkey test (Post Hoc Analysis). Statistical significance was
set at P < 0.05. Significant differences in the real part,
imaginary part and loss tangent of urine across the groups
were analyzed and plotted.
patients’ profiles. These samples were collected from patients
prior to surgery, they are still carrying the lumps and have
recently diagnosed as breast cancer patients (they have not
taken medicine and received chemotherapy).
Table 1 Profiles of patients participated in the study
Stage
Histo Type
Chinese
Tumour
Size
(cm)
11
3B
51
48
49
56
67
70
Malay
Chinese
Chinese
Malay
Indian
Chinese
1.2
1.5
3
2
3.5
1.4
1
1
2A
1
2A
1(DCIS)
8
9
10
52
77
68
Indian
Chinese
Chinese
1
2
2
2A
2A
3B
11
41
Chinese
0.7
1
12
13
14
15
16
17
18
19
36
53
53
71
53
51
46
43
Chinese
Chinese
Chinese
Chinese
Chinese
Chinese
Chinese
Chinese
3.2
6
5.5
3
3.1
1.4
5
6
2A
3A
2B
3C
2A
1
3B
3A
IDC (Invasive
Ductile
Carcinoma)
IDC
IDC
IDC
IDC
IDC
DCIS (ductal
carcinoma in
situ)
IDC
IDC
ILC (Invasive
Lobular
Carcinoma)
DCIS with
microinvasion
IDC
IDC
IDC
IDC
IDC
IDC
ILC
IDC
No
Age
(Year Old)
Ethnic Group
1
32
2
3
4
5
6
7
Urine Samples were taken after the subjects were refrained
from having food, exercising and even brushing teeth at least
30 minutes before performing the test. Complex permittivity
of the collected urine samples were measured using Network
Vector Analyzer model (Agilent Technology, Agilent E8362C
PNA network analyzer) using slim form probe with 2.4 mm
male connector. The probe features a slim design. The slim
design allows it to be used with smaller sample sizes. The
slim form probe was connected to a sealed slim form holder
that adapts 2.2 mm outer diameter to 10 mm inner diameter
bracket as well as a midi sized adapters and bushings.
Measurements were made by simply immersing the slim form
probe in the samples. Calibration was conducted before each
measurement. Calibration includes setting frequency between
10MHz to 20GHz, and configuring calibration using slim
form probe and temperature of 25 ÛC. Two electrical
properties of urine were measured, real part of permittivity
(İƍ) which is called dielectric constant and imaginary part of
permittivity (İƎ) which is called loss factor. Imaginary part
and real part values were transferred and arranged in Excel.
Based on the obtained real and imaginary parts’ values, loss
tangent values were calculated according to the equation:
Fig. 1 Real part of permittivity as a function of frequency. A.
Real part of stage 1. B. Real part of stage 2. C. Real part of
stage 3.
III.
RESULTS AND DISCUSSIONS
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was found over a frequency range between 6.6 GHz to 17.8
GHz.
Figures (3-5) represent the dielectric parameters as a function
of frequency for urine samples. Dielectric parameters with the
highest F number (the ratio of the difference between the
groups to the difference within the same group) were
compared between the groups of subjects. Moreover, the
output of the statistical analysis of urine’s permittivity values
reveals the existence of significant difference across the
groups for each type of permittivity parameters. As shown in
figures (3-5) the shape of the graphs for the mean of dielectric
properties of stage1, stage2 and stage3 subjects follow almost
the same trend. The dielectric properties of all stages in
dielectric property decreases with increasing frequency while
in loss factor and loss tangent the dielectric property of all
stages increases with increasing frequency. However another
common trend observed that stage1 appears to have the
leading value for all dielectric parameters, while stage 2 has
the least values for all dielectric parameters over the
considered microwave frequency range. Meanwhile, the
appearance of stage1 and stage2 has a seemingly overlapping
presentation for dielectric constant parameter at frequencies
greater than 18.6 GHz. It is obvious in all the graphs that the
curves for the mean of all the groups are far from each other
which indicates that a significance difference can be found
between the groups in the dielectric parameters of urine.
Moreover, in all the figures it can be seen that the graphs of
stage 3 is between the graphs of stage 1 and stage 2 which is
also agree with what was found by Qiao G. et al. for the
conductivity values. He observed that the conductivity values
of late stage breast cells is between the conductivity values of
early stage and invasive cancer cells [13].
Fig.2 mean of dielectric constant parameter of urine for
different stage of breast carcinoma
IV.
CONCLUSION
According to the results obtained from the statistical analysis
output, which are represented graphically and summarized in
tables, it can be concluded that permittivity of urine of
subjects with breast cancer has a potential in differentiating
between different stages of breast cancer. The significant
difference derived from the output of urine samples for all
dielectric parameters (dielectric constant, loss factor and loss
tangent) of permittivity can be considered as a starting point
for more investigation in the staging research using
microwave technique.
The significant difference between the groups is appreciable
because F numbers are quite high with the highest being
20.233 obtained at frequency of 18.4008 GHz for the loss
factor parameter with a significant level of p =.000 and the
least is F = 6.418 obtained at frequency of 13.003 GHz for the
loss tangent parameter with significant level of p =.009. From
table 2 it can be concluded that differentiation can be made
between stage1-stage2 groups and stage2-stage3 groups from
urine sample analysis considering dielectric constant
parameter as a function of microwave frequency. While in
loss factor parameter the significant level is divided into two
parts according to the groups which a significant difference
was found between them. The first part includes the groups:
stage1-stage2 and stage2-stage3, the difference was
significant mainly in the frequency range between 8.8-10.8
GHz. Differentiation between stage1-stage3 can also be made
from urine sample analysis considering loss factor parameter
as a function of microwave frequency range. The significant
difference was obtained over a frequency range between 4.6
GHz to 6.8 GHz which is low relatively compared to the
frequency range where the significant difference between
stage2-stage3 and stage1-stage2 was obtained. In loss tangent
parameter significant difference between stage1 and stage2
ACKNOWLEDGMENT
The authors wish to extend his sincere appreciation to
Universiti Malaysia Perlis (UniMAP) for giving the
opportunities to use the research facilities in the Bio-chip
Fabrication and characterization Lab and Ministry of Science,
Technology & Innovation (MOSTI) for providing us with
grant to carry out this research. The appreciation also goes to
all the team members in the Institute of Nanoelectronic
Engineering especially in the Nano Biochip Research Group.
[2] Gibbins, D., et al., A comparison of a wide-slot and a stacked patch antenna for the purpose of breast cancer
detection. IEEE transactions on antennas and propagation, 2010. 58(3): p. 665-673.
[3] Nandi, J.R., et al., Scutt D Classification of breast masses in mammograms using genetic programmingand feature
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