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Genomics and Applied Biology
2014, Vol.5, No.3
http://gab.sophiapublisher.com
applied to various problems
(Chaudhuri et al., 2007).
Medical classification problems are among them. In
this study we applied an Artificial Immune System
(AIS) to the problem of prostate cancer classification.
The used AIS models the clonal selection theory in IS
as many other AISs. 17 data from healthy people and
33 data from patients were used for this classification
process. The features of data are age, gleason score,
personal history of cancer and family history of cancer.
The 70% of data was taken for training and the
remaining 30% was used for the test process. After
analysing AIS with different parameters, best
classification accuracy on test data was recorded as
93.33%. In other words, only one data was incorrectly
classified.
The other type of classification with prostate cancer
data was conducted with artificial neural networks
(ANN) (Principe, et al., 1999). When this widely used
effective classifier was applied the performance has
increased to 100% by correctly classifying the whole
test data.
1 Materials and Methods
1.1 Dataset formation
Paraffine blocks of prostate pathologies were derived
from the archives of the Department of Pathology in
Faculty of Medicine at the University of Selcuk,
Turkey. Namely, Paraffine-embedded prostate cancer
tissue specimens of 50 subjects were used in this study.
Age range was 35-72 years, all subjects were males.
10 subjects had family history of cancer and 40
subjects were non family. These subjects went to
physicians to demonstrate a variety of serious
symptoms of prostate cancer, e.g.
,
difficulty in voiding,
urodynia, urgent and frequent urination, and hematuria.
Their prostates were examined by one or more of the
following means: rectal ultrasound detection, digital
rectal examination, computed tomography, and
magnetic resonance imaging. Biopsy was performed
for the subjects who were suspected to have prostate
cancer, and all specimens were from archived paraffin
blocks.
Diagnosis of prostate cancer requires the tissue and
cell specimens. These specimens are screened and
analyzed by a pathologist using a microscope.
Optimum medical treatment is decided according to
this information gathered by the pathologist. In some
cases, correct diagnosis is very hard and there can be
30-40% difference between pathologists’ decisions
(Schenck, and Planding, 1998). Dramatic results about
wrong diagnosis of cancer cases from biopsy slides
can be found in (Kopec et al., 2003). Prostate cancer is
evaluated using two staging systems: the
Jewett-Whitmore system and the TNM (tumor, node,
metastases) system. In TNM system, T refers to the
size of the primary tumor, N will describe the extent
of lymph node involvement, and M refers to the
presence or absence of metastases.
In this study, 50 data with 4 features were used. The
features are:
1.
Age
2.
Gleason score (PSA*)
3.
Personal History of Cancer
4.
Family History of Cancer
Here, PSA is a protein produced by the prostate gland
that can be detected in the blood. Levels rise with age
and when the prostate is enlarged. Significantly
increased levels of PSA in the blood can indicate
prostate cancer. PSA levels are also known to rise in
other prostate conditions such as prostatitis
(inflammation of the prostate). Normal values of PSA
are as the following:
AGE / PSA Value
Under 50 years / < 2.5
50 – 59 years / < 3.5
60 – 69 years / < 4.5
70 years and over / < 6.5
The personal and family history of cancer in 3
rd
and
4
th
features are taken as 0 if there is no presence of
cancer and 1 if there has been a cancer in the subject
or his family. The 17 data belong to the healthy
subjects while the remaining 33 data were of subjects
with prostate cancer. The 70% of the whole data was
taken for training, and 30% was taken for test. The