2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery
Diagnosis of endometrial cancer based on back-propagation neural network and near-infrared spectroscopy of tissue
Yuhong Xiang, Jing Tian, and Zhuoyong Zhang *
Yinmei Dai
Department of Chemistry, Capital Normal University,
Beijing Obstetrics and Gynecology Hospital, Affiliated to
Beijing 100048, P.R. China
Capital Medical University,
Corresponding E-mail address:
[email protected]
Beijing 100006, P.R. China
Abstract
comparing with the data of 6000 deaths in 1996 [2]. The
Endometrial cancer is one of the most common
new case is more common in women who are older, white,
cancers in women worldwide. Early stage and accurate
affluent, obese, and of low parity, and especially women
diagnosis is indispensable for treatment of endometrial
with postmenopausal bleeding should be screened for
cancer patient. In this study, near-infrared spectra of 18
endometrial
normal, 30 hyperplasia and 29 malignant pathological
measurements of endometrial thickness and hysteroscopy
sections were collected. The original spectra were
have also been studied, biopsy sampling is currently the
pretreated by using smoothing, denoising, and data
most accurate and widely used screening technique [3]. But
compression methods, 6 principal components were
the diagnosis of biopsy sample has some limitations. For
extracted as the input of back propagation neural network
example, it is difficult to distinguish between the
(BPNN). The number of hidden neurons, learning rate,
endometrial cancer and atypical endometrial hyperplasia [3].
momentum, and learning epochs were optimized based on
Therefore, it is necessary to develop an accurate, fast,
the RMSE of leave-one-out cross validation (LOOCV). The
convenient and inexpensive method to diagnose the
optimal model of BPNN built can successfully classify the
endometrial cancer at early stage.
cancer.
Although
ultrasonographic
samples into three groups. The results showed that BPNN
Near-infrared spectroscopy (NIRS) has been known as
coupling with NIR spectroscopy can provide an efficient
an accurate, fast and inexpensive method for pathology
method for the early diagnosis of endometrial cancer.
studies. NIR spectra (750–2500 nm) can be assigned mostly to overtones and combinations of the molecular vibrations
Keywords-endometrial cancer; near-infrared spectrum;
of C–H, N–H, and O–H groups. These groups are
back propagation neural network;
composition of all biological molecules (lipids, proteins,
1. Introduction
carbohydrates, and water) [4, 5]. Cancer tissues differ from their normal parts in their composition. Any alteration in the composition of the tissues can be detected and used for
Endometrial cancer is one of the most common
diagnostic purposes.
gynaecological cancers. The data of the National Cancer
NIR
Institute showed 40100 new cases and 7470 deaths from
spectroscopy
technology
has
important
endometrial cancer in United States in 2008 [1]. Deaths
applications in cancer diagnosis. Most of researches dealing
from endometrial cancer are prone to continue rising
with human tissue were on breast cancer [6-9]. Besides
978-0-7695-3735-1/09 $25.00 © 2009 IEEE DOI 10.1109/FSKD.2009.470
508
breast cancer, the diagnoses of prostate cancer [10, 11],
were supplied by Beijing Obstetrics and Gynecology
pancreatic and colorectal cancer [4, 12] have also been
Hospital, Affiliated to Capital Medical University. The
reported. The method of cancer diagnoses above was to
mean age is 46 with the oldest 71 years and the youngest 19
quantify tissue chromophores or differentiate between the
years. All the endometrial tissues were fixed in 4%
absorption and scattering parameters. However, peaks in
formaldehyde, embedded in paraffin wax, section, xylol
many near-infrared spectra are overlapped, and in some
deparaffinization, dehydration with a series of increasing
cases, sloping baselines of spectra are observed. Therefore,
alcohol concentrations, adhibit, neural gum mounting. The
it is difficult to assign NIR absorbance bands. Computer
thickness of all the paraffin section was assumed to be 4
technology coupling with chemometrics can provide a
µm. The gold coated mirror onto the paraffin sections were
powerful tool for NIR applications [13-16]. Neural networks are mathematical models inspired by
placed on the integrating sphere. The NIR diffuse reflection
biological neurons. Back propagation neural networks
spectra were collected by the FT-NIR spectrometer
(BPNNs) are feed-forward network trained by propagation
controlled by Thermo Fisher Omnic software of version 7.3
of error back through the networks. Neural networks are a
with optical resolution of 4 cm− over the spectral range of
better method for pattern recognition and calibration than
4500 and 10000 cm− . A background spectrum was recorded
conventional statistical methods because of their advantages
using air reference at 25°. Five times was scanned for each
with
self-organization,
self-study,
self-adoption,
1
1
section on different positions, the mean was used. All the
and
NIR spectra of 77 paraffin sections were shown in Fig. 1.
unusual fault tolerance. In this work, NIR spectra of a total of 18 normal, 30 hyperplasia, and 29 malignant tissue slices were collected. The
original
Savitzky-Golay
spectra
were
smoothing
and
by
using
multiplicative
scatter
pretreated
correction (MSC). According to the standard deviation spectra of 77 pathological sections of endometrial tissues, the data of NIR spectra in the region of 4000-4600 cm-1 were selected as candidate input invariables. Principal component analysis (PCA) was used to compress the spectral data. The first 6 principal components were employed as the input variables of BP neural network. In the process of building BPNN, the RMSE of leave-one-out
Fig. 1. The NIR spectra of 77 tissue sections
cross validation was used as the goal function to optimize the number of hidden neurons, learning rate, momentum
From the Fig. 1, it can be seen that it is difficult to
and learning epochs. The optimal BP neural network built
distinguish the difference of NIR spectra of normal,
can successfully classify the samples into three groups. The
hyperplasia, and malignant endometrial tissues.
results showed that the BP neural network coupling with
In this study, according to the standard deviation
NIR spectra of endometrial tissue can provide an efficient
spectra of 77 pathological sections, the NIR spectra in
method for the early diagnosis of endometrial cancer.
region of 4000-4600 cm
-1
were selected for further
identification. NIR spectra of all the samples were firstly
2. Material and method
smoothed by the Savitzky-Golay (polynomial function order 3, smooth windows 5) method. Then, MSC was
Seventy seven paraffin sections of endometrial tissues
applied to correct spectra for spectral noise and background
509
effects which cause baseline shifting and tilting. Principal
input and output variables. However, if the number of
component analysis was employed to reduce the number of
hidden layer neuron is too large, the experimental error
input variables. The first 6 principal components accounting
(noise) will be introduced into the BPNN. In our work, the
for 99.97% original information of NIR spectra were
optimization process of neurons in hidden layer was shown
selected as the input variables of BP neural network. The
in Fig. 2.
transfer functions of neurons in hidden layer and out layer were ‘‘tansig’’ and ‘‘purelin’’, respectively. The samples were divided into two sets, training set and test set, by using Kennard-Stone method [17]. The training set including 58 samples was used to build the BP neural network model, and the 19 samples were used to evaluate the model. In the process of building BP neural network, leave-one-out cross validation was used to estimate the generalization properties of statistical models. Binary encoding, (1 0 0), (0 1 0) and (0 0 1), were used to express the diagnosis results for normal, hyperplasia, and malignant, respectively.
Fig. 2. Influence of the number of neurons in hidden layer on network
3. Results and Discussions
Note: the classification accuracy of leave-one-out cross validation (LOOCV) and test set were plotted with ○ and ●, and the
Traditionally, BPNN consists of three layers. In this
RMSE of LOOCV and test set were plotted with ▽ and ▼,
work, the traingdm function was used to train the network.
respectively (similarly hereinafter in below figures).
The optimal neural network model was obtained by
From the Fig. 2, it can be seen that the BPNN with 5
optimization of the number of neurons in hidden layer,
hidden neurons had the lowest root mean square error of
learning rate, momentum, and learning epochs. The goal
leave-one-out cross validation (RMSECV). And the
function RMSE of leave-one-out cross validation was used
classification accuracy (CA) rate for leave-one-out cross
to optimize the architecture and parameters of BPNN.
validation (LOOCV) and test set were 100% and 94.74%,
RMSE was calculated according to (1)
respectively.
The influence of learning rate (1) The
Where, yci and yei are calculated and experimental values,
learning
rate
is
an
important
parameter
influencing the stability and training speed of the BPNN. If
respectively, and p is the number of samples.
learning rate is too small, the slow convergence and local minima may be happen. However, if learning rate is too
The influence of hidden neural neurons
large, the convergence speed increases but the performance of neural network can not work well because the network
The number of hidden layer neurons plays a crucial
may jump over the global minimum. The optimization
role on the generalization ability of BPNN. If the number of
process of learning rate was shown in Fig. 3.
hidden layer neuron is too small, neural network can not simulate the complicated function relationships between
510
Learning epochs also influence the accuracy of BPNN. If the number of learning epoch is too small, the desired convergence will not be reached. Whereas, overfitting will be happen. The optimization process of learning epochs was shown in Fig. 5, in which the learning epoch varied from 1000 to 10000. From the Fig. 5, it can be seen that 7000 epochs with the lowest RMSECV was considered as the optimal learning epochs. The CA rate of leave-one-out cross validation and the test set were 100% and 94.74%, respectively. Fig. 3. Influence of learning rate on network
From the Fig. 3, it can be seen that when the learning rate reaches 0.6, the lowest RMSECV was obtained. The CA rate for leave-one-out cross validation and test set are 100% and 94.74%, respectively.
The influence of momentum Adding a momentum term can help to escape a small local minimum in the error surface and speed up the convergence. But if momentum is too large the BPNN will
Fig. 5. Influence of learning epochs on network
lead to small oscillations rather than divergence. From the optimization process shown in Fig. 4, it can be seen that
There was a wrong classification sample with number
when the momentum is 0.5, the lowest RMSECV and the
index 48 in test set. Sample 48 belongs to hyperplasia class.
highest CA rate was obtained.
Malinowski Distances between sample 48 and the center of malignant and hyperplasia class were investigated. The results showed that sample 48 was further from the center of hyperplasia than that of malignant. Therefore, sample 48 maybe an outlier, although further proof is still needed.
4. Conclusion The BP neural network with 6 input, 5 hidden, and 3 out neurons was built to diagnose the endometrial cancer based on the NIR spectra of endometrial tissue section. The
Fig. 4. Influence of momentum on network
classification
accuracy
rate
of
leave-one-out
cross
validation and test set were 100% and 94.7%, respectively.
The influence of learning epochs
The results indicated that neural network coupling with NIR spectroscopy of tissue can provide a new method for the
511
early diagnosis of endometrial cancer. Further work of this
York, 2005, 4(5): 497-512.
aspect is under investigation.
[10] J.H. Ali, W.B. Wang, M. Zevallos, and R.R. Alfano, Near infrared spectroscopy and imaging to probe differences in water content in normal and cancer human prostate tissues, Technol.
Acknowledgment
Cancer Res. Treat., Adenine press, New York, 2004, 3 (5): 491-497.
This work was supported by the Natural Science
[11] M.H. Rhiel, M.B. Cohen, M.A. Arnold, and D.W.
Foundation of China (20875065 and 30772322).
Murhammer, On-Line Monitoring of Human Prostate Cancer Cells
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