
Research Article


Assessment of Gastric Cancer Survival: Using an Artificial Hierarchical Neural Network 

Zohreh Amiri,
Kazem Mohammad,
Mahmoud Mahmoudi,
Hojjat Zeraati
and
Akbar Fotouhi



ABSTRACT

This study is designed to assess the application of neural networks
in comparison to the KaplanMeier and Cox proportional hazards model in
the survival analysis. Three hundred thirty gastric cancer patients admitted
to and surgically treated were assessed and their postsurgical survival
was determined. The observed baseline survival was determined with the
three methods of KaplanMeier product limit estimator, Cox and the neural
network and results were compared. Then the binary independent variables
were entered into the model. Data were randomly divided into two groups
of 165 each to test the models and assess the reproducibility. The Chisquare
test and the multiple logistic model were used to ensure the groups were
similar and the data was divided randomly. To compare subgroups, we used
the logrank test. In the next step, the probability of survival in different
periods was computed based on the training group data using the Cox proportional
hazards and a neural network and estimating Cox coefficient values and
neural network weights (with 3 nodes in hidden layer). Results were used
for predictions in the test group data and these predictions were compared
using the KaplanMeier product limit estimator as the gold standard. Friedman
and KruskalWallis tests were used for comparisons as well. All statistical
analyses were performed using SPSS version 11.5, Matlab version 7.2, Statistica
version 6.0 and S_PLUS 2000. The significance level was considered 5%
(α = 0.05). The three methods used showed no significance difference
in base survival probabilities. Overall, there was no significant difference
among the survival probabilities or the trend of changes in survival probabilities
calculated with the three methods, but the 4 year (48th month) and 4.5
year (54th month) survival rates were significantly different with Cox
compared to standard and estimated probabilities in the neural network
(p<0.05). KaplanMeier and Cox showed almost similar results for the
baseline survival probabilities, but results with the neural network were
different: higher probabilities up to the 4th year, then comparable with
the other two methods. Estimates from Cox proportional hazards and the
neural network with three nodes in hidden layer were compared with the
estimate from the KaplanMeier estimator as the gold standard. Neither
comparison showed statistically significant differences. The standard
error ratio of the two estimate groups by Cox and the neural network to
KaplanMeier were no significant differences, it indicated that the neural
network was more accurate. Although we do not suggest neural network methods
to estimate the baseline survival probability, it seems these models is
more accurately estimated as compared with the Cox proportional hazards,
especially with today`s advanced computer sciences that allow complex
calculations. These methods are preferable because they lack the limitations
of conventional models and obviate the need for unnecessary assumptions
including those related to the proportionality of hazards and linearity.







INTRODUCTION
Survival probability assessments date back to several decades now.
One of the every day concerns in various biological and medical sciences
is determining the median lifetime in different age groups of a population
and associated factors so that health and survival time can be improved.
Individual survival time in every population is random and therefore,
it can be estimated only through statistical methods. This explains the
many efforts made by biostatisticians, especially since the seventies.
Survivaltime and survival data exhibit characteristics,such as being
censored or truncated, that make them unsuitable for analysis in traditional
statistical methods used in other fields of statistics. One of the major
activities in health research is the assessment of survival time or time
to recurrence in patients receiving treatment to examine the effectiveness
of treatment modalities. Such methods are frequently used in fertility
studies, medical demography and social researches as well.
In recent years, survival studies have evaluated the eventfree probability
until time t (survival probability until time t) and of various variables
influencing this eventfree period (lifetime) using different methods
including parametric models, life table estimations, the KaplanMeier
estimator to estimate survival probability and models such as the Cox
proportional hazards model, models based on stochastic processes and neural
networks to analyze the effect of different variables on survival probability,
(Klien and Moeschberger, 1997). Unlike nonparametric methods, survival
analysis with parametric methods requires the probability density function
to estimate survival and hazard functions. Basic methods such as lifetime
tables are events occurring only within the evaluated period and therefore,
survival time is not assessed in between these periods and part of the
data is not used. In the KaplanMeier method, the simultaneity of the
data does not receive enough attention and it is assumed that the occurrence
of any given event is possible only during a short interval (Kaplan and
Meier, 1958). The Cox proportional hazards model is recommended for the
assessment of the effects of different variables on lifetime. This model
makes certain assumptions such as the distribution being proportional
and exponential family, while these assumptions are rarely considered
in applied studies. Therefore, it seems necessary to develop models that
are independent of such assumptions. In recent years, use of neural networks
in medical research of diagnostic processes has been suggested, but there
are limited studies done in other fields of medical research or survival.
Researchers have always wondered how the human mind performs and many
efforts have been made to design similar models. During the 1950s, this
issue was more closely followed along with advances in computer sciences.
One important step was biologic neural network simulation with computers.
In 1951, McCullough and Pitts presented the first descriptions of artificial
neural networks (Papik et al., 1998; Jerez et al., 2005).
Later, there was more information concerning how the human neural system
functions and thus, better simulations were made possible. Mathematical
methods for neural network design were further developed through the works
of McLelland and Rummelhart from 1982 to 1987. Patterning the neural plate
is a new concept and its use has been

Fig. 1: 
Schematic view of a biologic neuron 
discussed and evaluated in different fields(Jerez et al., 2005;
Fuxe et al., 2007). Although traditional statistical models and
neural networks are similar in some ways, their key difference lies in
the fact that traditional models mainly focus on finding answers in linear
environments while neural networks are nonlinear (Bakker et al.,
2004; Park and Chung, 2006; Hirsch et al., 2001; Marsland et
al., 2002).
Artificial neural networks can be considered mathematical algorithms
that make essential deductions based on modest data available in primary
units (Papik et al., 1998; Anagnostopoulos and Maglogiannis, 2006;
De Laurentiis and Radvin, 1994). In biological models, the neuron is the
processing element (PE) (Fig. 1). Every neural network
contains a number of PEs. These PEs are interconnected through input leads.
Input signals are either added to the data or transferred to the next
unit through axons (Papik et al., 1998; Sato et al., 2005).
Neural networks are mathematical models emulating the properties of the
human nervous system. Nerve fibers generate outputs by processing the
input. A neural network receives the inputs and presents one or many outputs.
The output value of every neuron is binary, but it can be modeled as a
continuous variable. The inputoutput relation of a single neuron can
be described using mathematical functions. These mathematical relationships
explain the behavior of neurons. In these cases, any neuron with a constant
input produces a constant and equal output. In these models, the only
variable is the connect ability between neurons. Although a constant input
to a function gives constant results, the connection between neurons tends
to vary in time. Therefore, in a network of neurons, the functionality
and connectivity is affected by the variable connection and the system
is continually changing and learning. The behavior of a network and its
changes in time are determined by the way the neurons in the network are
connected. PEs are usually arranged in layers. There are three layers
in these models: one input layer consisting of independent variables,
one output layer related to the dependent variable and one or several
intermediate layers known as the hidden units. All PEs in each layer are
connected to all PEs in other layers.
The main issue in every neural network is finding the model coefficient(s)
that transform input data to output in the layer (or middle layers) with
minimum error. Usually the weight sum of the input plus a constant value
(bias) in the middle layer is under the effect of a constant coefficient
(e.g., logistic). Weights are determined by minimizing the sumofsquares
error function or minus the logarithm of likelihood.
In health medicine research, neural networks are mostly used for making
diagnoses and there are few reports on the use of such models in medical
studies. Some of these studies have assessed the results of cardiopulmonary
resuscitation measures (Ebell, 1993), antiaddiction programs (Ashutosh
et al., 1992), tumor advancement in cancer studies (Burke, 1994;
Radvin et al., 1992) and hepatic transplant failure (Doyle et
al., 1994). This method has been used to study the predictive value
of serum enzymes for myocardial infarctions and a sensitivity of 100%
and a false positive rate of 8% were determined (Baxt, 1991). Another
team used EKG in addition to serum enzyme levels to increase the accuracy
of their predictions (Baxt, 1992).
Use of such models has been very limited in survival studies as well.
In 1992, Ravdin et al. (1992) first used these models in a survival
study of patients with breast cancer and demonstrated that these models,
compared to traditional methods, can generate relatively more accurate
results. In their study, time was entered in the model as a predictive
variable. For each patient, the number of intervals during which the patient
was alive was considered a variable. In 1996, an article was published
by OhnoMachado (1996) in which the use of multiple neural networks in
survival analyses was suggested. Data pertaining to the main event occurring
at the same time were set in the same neural network and the output of
each network was studied in the more general model. In this study, censored
data were entered in the model as well.
In their article titled Neural networks as statistical methods in survival
analysis, Ripley and Ripley (2001) present a comparison between classical
methods and those based on neural networks in studying data from breast
cancer patients. Their main purpose was to substitute a linear function
with a neural network. They believe neural networks resemble powerful
cars; it may be difficult and sometimes confusing to drive them well and
so sometimes using a simpler would be more suitable. They used the SPlus
software in Unix and PC environments, but wrote the required codes themselves.
They also believe overfitting is the major issue in implementing neural
networks and considering the sensitivity and specificity of the used model,
they concluded the supporting evidence was not enough to make one model
preferable to the other. In another report titled Nonlinear survival
analysis using neural networks. Ripley and Harris (2004) published the
use of neural networks in the analysis of data from 1335 patients with
breast cancer. In this study, they assessed survival until the first recurrence
after surgery as the independent variable and 11 personal, diagnostic
and treatment variables as independent ones. Missing data was estimated
through multiple linear regression and analysis was done on 680 patients
with missing data. Variables were coded in a binary format and analyses
were based on multilayer perception models. These models are free of
the assumptions used in conventional regression models. The report indicates
the use of 7 different neural networks and their efficiency in predicting
recurrence time for breast cancer has been discussed. In the first model,
recurrence is divided into two periods; in the next 2 models, recurrence
time is divided in 5 periods in two different ways; and in the remaining
4 models, recurrence time is considered continuous (log logistic, proportional
hazard, log normal and the developed model of proportional hazard). They
eventually conclude that the models were not very effective.
In terms of network architecture, the neural network known as the multilayer
perception (MLP) is used most commonly. An MLP consists of an input layer
of variables, an output layer and one or several hidden units. In these
models, each input (x_{i}) has a corresponding weight (w_{ij}).
A certain function (φ_{k}) affects the sum of weighted input
plus a constant value like α_{j} (usually equals 1) which
is equal to the bias. In most cases a logistic function is used. Thus,
for the k^{ th} output (y_{k}):
In Iran, there is no reliable information on the number of cancer patients
or the number of new cases per year, because the cancer registry system
is inefficient and inaccurate. However, the standardized incidence in
Tehran for year 1999 was estimated 130.9 and 109.8 per 100,000 for men
and women, respectively. The exact number of deaths due to cancer is not
known either, but estimates indicate more than 27 thousand deaths due
to cancer in the approximately 70 million population of Iran in 1999 (Mohagheghi,
2004). Several reports have stated that gastric cancer has a high prevalence
in Iran (ranking second among men and fourth in the general population)
and since most patients present in advanced stages, the disease has a
high mortality rate (Mohagheghi et al., 1998, 1999; Mohagheghi,
2004).
In cancer research, it is important to determine the probability of survival.
Several studies have been conducted in the regard in different countries.
The 5 year survival of gastric cancer patients after surgery has been
reported 29.6% in China (Ding et al., 2004), 4.4% in Thailand (ThongNgam
et al., 2001), 37% in the United States (Schwarz and ZagalaNevarez,
2002) and 30% in France (Triboulet et al., 2001). Various determinants
of survival have also been studied including age, disease stage and presence
of metastasis.
In Iran, the lifetime of cancer patients has been studied in different
projects, including one based on the data bank of the present study with
a 5 year survival of 23.6% and a median lifetime of 19.90 months. In these
studies, the Cox proportional hazards model was used to show that variables
of age, presence of metastases and disease stage can greatly affect the
chance of survival (Zeraati et al., 2005).
As mentioned earlier, conventional models such as KaplanMeier and Cox
proportional hazards model, although easily done with statistical software,
require assumptions that are usually disregarded. For instance we can
mention the assumptions used for simplifying models (e.g., assuming correlations
and models to be linear), disregarding the effect of independent variables
on each other, the doubtful assumption of the hazards being proportional,
distribution uncertainty and errors related to curve fitting (Jones et
al., 2006; Biganzoli et al., 2003). In the last 20 years, neural
networks have been used in the subjects related to classification and
failure prediction and they have found their place in classification but
not prediction (Jones et al., 2006; Suka et al., 2004).
In the present study, we examine the hypothesis that use of neural networks
has results similar to the Cox proportional hazards model and KaplanMeier.
MATERIALS AND METHODS
In this study, 330 gastric cancer patients who were admitted to
and underwent surgery at Iran Cancer Institute between 1995 and 1999 were
enrolled. Patients` lifetime after surgery was determined. Those who survived
after the date the study ended and those who were lost to follow up from
a certain date were right censored from that date. During the study period,
239 patients deceased; 13 with other causes of death who were right censored
from the date of death.
In the first step, three methods of KaplanMeier, Cox proportional hazards
model and the neural network were used to compute the observed baseline
survival time, regardless of the independent variables and then the results
were compared with the 95% confidence limits of the KaplanMeier limit
estimates using the BorganListol method. In the next step, independent
variables were coded in a binary form: age (<70 years = 0,>= 70 years
= 1), gender (female = 0, male = 1), site (cardia = 1, other = 0), pathology
(adenocarcima = 1, other = 0), presence of metastasis (no = 0, yes = 1),
Tstage (1 and 2 = 0, 3 and 4 = 1), Nstage (0 = 0, 13 = 1) and Mstage
(0, 1). Then probabilities of survival in time were calculated by entering
these variables into the model. For testing the models and assessment
of reproducibility, data were randomly divided into two groups of 165
cases each. We used multiple logistic regression and Chisquare tests
to ensure group similarity and random division of the data and logrank
test to compare subgroups. Then probabilities of survival were computed
for 6, 12, 18, 24, 36, 48 and 60 months of time through the Cox proportional
hazards model and the neural network. For this purpose, Cox coefficients
and estimates and neural network weights (with 3 hidden layers) were calculated
based on data from the reference group and used for predictions in the
study group. Predictions made with these two methods were compared with
the KaplanMeier limit estimates as the gold standard. Friedman and KruskalWallis
tests were used to make these comparisons. Staging was done according
to the 6th edition of the TNM system. Analyses were done using SPSS version
11.5, Matlab version 7.2, Statistica version 6.0 and S_PLUS 2000 and the
level of significance was considered 0.05.
RESULTS
Based on the report by Zeraati et al. (2005), 69.1% of the
patients were male and their median age was 68 years (range, 32 to 96).
The pathology was adenocarcinoma in 85.2% of patients and in the remaining
patients it was other pathologies (squamous cell carcinoma, small cell
carcinoma, carcinoid tumor, sarcoma, stromal tumor, malignant lymphoma,
or spindle cell tumor). One hundred and 92 patients (58.2%) had metastasis
and the type of surgery was total gastrectomy (TG) in 55.7%, subtotal
gastrectomy (SG) in 27.2%, partial gastrectomy (PG) in 8.8%, proximal
gastrectomy (PXG) in 8.5% and distal gastrectomy (DG) in 3.1%. Esophagojejunostomy
was performed in 50.9%, gastrojejunostomy in 27.6%, esophagogastrostomy
in 13.6%, colon bypass in 3.3%, Billroth II in 3.1% and colostomy in 1.5%.
Studying the stage of the disease showed that 3% were in stage IA, 3.6%
in IB, 18.2% in II, 13% in stage IIIA, 3.3% in IIIB and 58.8% in IV. All
cases of stage IV disease had N3 or T4, or had T3 and M1. While 20.3%
of patients had never received any secondary treatment, 26.1% of them
had alternate treatments 3 times. The 5 year survival probability for
the studied patients was 23.6%, the survival probability in the first
year was 66.7% and the median lifetime in the study was 19.90 months (Zeraati
et al., 2005).
In the first step, all data were used to compute the observed baseline
survival in time and using the three methods of KaplanMeier limit estimator,
Cox proportional hazards model and neural network the overall survival
probabilities were determined regardless of the independent variables.
By calculating the 95% confidence limits for KaplaniMeier limit estimates
using the BorganListol method, it was observed that these intervals did
not include estimates by the other two methods and there were no significant
differences between estimates generated by these three methods (Fig.
2).
In the second step, data were randomly divided into two groups (reference
and study) of 165 cases each. To ensure there was no significant difference
between the distributions of independent variables in these two groups,
we used chisquare tests (Table 1) and multiple logistic
regression models (Wald`s statistics = 0.10 to 2.46), the logrank test
was used to ensure patients` lifetime in the two groups were similar (p
= 0.58) and it was determined that there were no significant differences
between the two groups.
The logrank test was used in each group (reference and study) to assess
the effect of binary independent variables on patients` lifetime and results
indicated that gender, pathology type, presence of metastasis, Tstage,
Nstage, or Mstage had no significant effect on people`s lifetime, while
in both groups, age and history of receiving supplementary treatment significantly
affected lifetime (p<0.0001). Additionally, we used the Cox proportional
hazards model to assess the interactions between these variables on lifetime
in each group. Results indicated significant correlations with age, presence
of metastasis, history of receiving supplementary treatment and Nstage
(p<0.05).

Fig. 2: 
Baseline survival probabilities with all three methods
and the 95% confidence intervals 

Fig. 3: 
The neural network with three nodes in hidden layer
used in this study 
In the third step, probabilities of survival were computed for 6, 12,
18, 24, 36, 48 and 60 months of time through the Cox proportional hazards
model and the neural network with 3 hidden layers (Fig. 3).
For this purpose, Cox coefficients and estimates and neural network weights
were used for predictions in the study group and predictions made with
these two methods were compared with the KaplanMeier limit estimates
as the gold standard (Fig. 4). We found no significant
difference between predictions made with these two methods and results
obtained from the KaplanMeier method, except for Cox predictions for
48 and 54 months; the predicted five year survival probability in this
method was not significantly different from standard probabilities in
the KaplanMeier method though. To use the neural network, we first estimated
model weights based on data from the reference group and these weights
were used to predict the lifetime of patients in the study group. This
prediction was then used in the KaplanMeier method and the probabilities
of survival were estimated in the study
Table 1: 
Distribution of independent variables in the reference
and prediction groups and results of their comparison 


Fig. 4: 
Predicted probabilities of survival in the study group
with Cox proportional hazards model and the neural network in comparison
with KaplanMeier (95% confidence intervals) 
group. With the neural network, the probabilities of survival were insignificantly
higher than with the standard method up to around the 22nd month, generally
lower than standard values afterwards and then very close to standard
during the final months of the study (around the 42nd month). Findings
were compared using the logrank test and there were no significant differences
among the probabilities of survival by the three methods. Although the
Friedman test showed no significant difference in the trend of changes
in survival probabilities generated from these three methods, the KruskalWallis
test demonstrated that the 4 year (month 48) and 4.5 year (month 54) survival
probabilities with Cox were significantly different from that with the
standard method and the predicted probabilities with the neural network
(p<0.05). In the study group, the mean standard errors of the survival
probabilities with the three methods of KaplanMeier, Cox estimates and
the neural network were 0.03366, 0.03607 and 0.03386, respectively. The
standard error ratio of Cox estimates and the neural network to the KaplanMeier
method were 1.0717 and 1.0062, respectively, which although not significantly
different from each other or from the standard (KaplanMeier), indicated
better accuracy for the neural network.
DISCUSSION
The 5 year survival probability for the patients in this study was
23.6% with KaplanMeier and 22.3% with the neural network, which are similar
values and are both lower than that in other countries such as the United
States (Schwarz and ZagalaNevarez, 2002), France (Triboulet et al.,
2001) and China (Ding et al., 2004).
As expected, the observed baseline survival in time with KaplanMeier
limit estimator and Cox proportional hazards model gave similar results,
but was slightly different with the neural network, showing a higher survival
up to the fourth year and then close to the other two methods; differences
that were not statistically significant. In the study by Jones et al.
(2006), the neural network showed higher probabilities of survival throughout
the study with less difference after the third year. In fact we do not
expect neural networkbased models to generate appropriate estimates regardless
of the training constraints. Apart from classification and discrimination
which are important features of neural networks, in studies like the present
one, prediction is an essential issue. In a neural network model, the
objective is to design a suitable network (a nonlinear model and estimating
the weights of the model) that is capable of making predictions for new
entries and correcting the model at every stage with the new information
so that it would generate more accurate predictions. The baseline survival
computed with the neural network may not be very reliable, because it
is greatly affected by the sample number and more importantly, the function
is in the middle layer (logistic). The starting point in a neural network
model is the input layer (i.e., where independent variables are introduced)
and when their effect is ignored, there is no appropriate standard to
correct the coefficients in the model except data on independent variables
in the reference group (affected by the sample size) and the function
used in the middle layer. This is confirmed by other studies as well (Ravdin
and Clark, 1992; Ripley and Ripley, 2001; Jones et al., 2006).
Therefore, when computing the baseline survival from a databank, we recommend
with the KaplanMeier product limit estimator which can easily be done
by statistical software such as SPSS and STATA and suggest arcsinus or
BorganLiestol methods to determine the confidence intervals.
In the main analysis, when we used Cox proportional hazards and a neural
network with three nodes in hidden layer (Fig. 3) to
calculate Cox estimates and coefficients and neural network weights based
on data from the reference group, used the results to predict survival
probabilities at 6, 12, 18, 24, 36, 48 and 60 months and compared the
predictions with estimates from KaplanMeier as the gold standard (Fig.
4), we found that neither prediction had significant differences with
that by KaplanMeier, except for Cox predictions at 48 and 54 months;
although the predicted five year survival probability in this method was
not significantly different from standard probabilities in the KaplanMeier
method. With the neural network, the probabilities of survival were insignificantly
higher than with the standard method up to around the 22nd month, generally
lower than standard values afterwards and then very close to standard
during the final months of the study (around the 42nd month). In the study
group, the mean standard errors of the survival probabilities were 0.03366,
0.03607 and 0.03386 with the three methods of KaplanMeier, Cox estimates
and the neural network, respectively. The standard error ratio of Cox
estimates and the neural network to the KaplanMeier method were 1.0717
and 1.0062, respectively, which although not significantly different from
each other or from the standard (KaplanMeier), indicated better accuracy
for the neural network. Use of such models has been very limited in survival
studies as well. Ravdin et al. (1992), who have used neural networks
in a survival study of patients with breast cancer, also report that these
models can generate relatively more accurate results compared to traditional
methods. In their study, missing data were not considered and time was
entered in the model as a predictive variable. For every patient, the
number of time intervals the patient survived was considered an independent
variable and other independent variables were not considered very much.
Therefore, our findings are more valid. In our study, an overestimation
of survival probabilities is seen compared to the standard method. A similar
observation was made by Ripley and Ripley (2001), but they claimed there
was not enough evidence to make one method superior to others. In a study
by Ripley and Harris (2004), in which they assessed survival until the
first recurrence after surgery as the independent variable and 11 personal,
diagnostic and treatment variables as independent ones and variables were
coded in a binary fashion and analyses were done based on a multilayer
perception, it was concluded that use of neural network models may not
be very beneficial. Jones et al. (2006) observed results similar
to ours and concluded neural networks were more accurate than Cox models
in making predictions. Similarly, independent variables were grouped in
a binary method in their study and three nodes in hidden layer were used.
CONCLUSION
In light of advancements in computer sciences, neural networks are
expected to improve in terms of practicality and effectiveness and although
we do not recommend them for estimating baseline survival, they do seem
to predict survival probabilities more accurately than the Cox proportional
hazards model, especially now that limitations regarding sophisticated
computations have been removed. These methods are preferred because they
lack the limitations of conventional models and obviate the need to accept
unnecessary assumptions such as those related to proportionality of hazards
and linearity. Taking independent variables as continuous variables dependent
on time is another issue under investigation and publication by the authors
of the present report.

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