Journal of Cancer Therapy, 2013, 4, 1472-1477
Published Online December 2013 (http://www.scirp.org/journal/jct)
http://dx.doi.org/10.4236/jct.2013.410177
Open Access JCT
Forecasting of Survival Rate in Patients with the Early
Stage of Non Small Cell Lung Cancer
Oleksey P. Kolesnik1, Anatoliy I. Shevchenko1, Yuriy E. Lyakh2, Vitaliy G. Gurianov2
1Department of Oncology, Zaporizhzhya State Medical University, Zaporizhzhya, Ukraine; 2Department of Medical Biophysics,
Medical Informatics and Biostatistics, M. Gorky Donetsk National Medical University, Donetsk, Ukraine.
Received November 6th, 2013; revised December 1st, 2013; accepted December 9th, 2013
Copyright © 2013 Oleksey P. Kolesnik et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In
accordance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intel-
lectual property Oleksey P. Kolesnik et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
ABSTRACT
Lung cancer is the most common cause of death from oncological diseases all over the world. Primary treatment of pa-
tients with the early stage of non-small cell lung cancer is a surgery. However, after surgery 30% - 85% of patients un-
dergo disease progression. In order to improve the results of treatment of patients with non-small cell lung cancer it is
necessary to separate a group of patients with dismal prognosis for whom adjuvant chemotherapy will permit improving
the survival rate. The aim of our research was to create a forecasting model with a view to detect the patients with the
early stage of non-small cell lung cancer and dismal prognosis. Our research covered 254 patients with the early stage
of non-small cell lung cancer who underwent a cure from June 2008 till December 2012 in the department of thoracic
surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. In order to identify the factors connected with the
risks of low survival rate of patients with the early stage of non-small cell lung cancer after curative treatment (surgical
treatment, adjuvant chemotherapy), a method of design of neural network models of classification was used. 39 factors
were taken for input characteristics. During investigation two forecasting models were built. As follows from the analy-
sis of first forecasting model with the increase of the patient’s BMI, the risk of low patient survival rate statistically and
significantly (p = 0.03) decreases, OR = 0.89 (95% CI 0.80 - 0.99) for each kg/m2 index value. The risk of low patient
survival rate also decreases (p = 0.02) if he has a squamous cell carcinoma, OR = 0.36 (95% CI 0.15 - 0.88) compared
with other histological forms of tumor. The connection between the risk of low patient survival rate and the volume of
surgical intervention was discovered (p = 0.01), OR = 3.19 (95% CI 1.29 - 7.86) for patients who underwent a pul-
monectomy compared with patients who underwent an upper bilobectomy. As follows from the analysis of second
forecasting model with the increase of the patient’s BMI the risk of low patient survival rate statistically and signifi-
cantly (p = 0.01) decreases; OR = 0.84 (95% CI 0.74 - 0.96) for each kg/m2 index value. It is found that with the in-
creasing level of the EGFR expression in the primary tumor, the risk of low patient survival rate statistically and sig-
nificantly increases (p = 0.04), OR = 1.39 (95% CI 1.01 - 1.90) for each graduation rate. The risk of low patient survival
rate also increases when conducting the lymph dissection in the volume D0 - D1.
Keywords: Forecasting Model; Survival Rate; Non-Small Cell Lung Cancer
1. Introduction
Lung cancer is the most common cause of death from
oncological diseases all over the world. Non Small Cell
Lung Cancer (NSCLC) makes up 80% - 90% of all ma-
lignant neoplasm of lung. Primary treatment of patients
with the early stage of NSCLC is a surgery. However
after surgery 30% - 85% of patients undergo disease
progression, wherefore five-year survival rate of this
group of patients makes up 40% - 70%. Herewith 22% -
50% of patients have local regression, 48% - 78% have
distant metastases and 3% - 20% of patients have simul-
taneous regression [1-5].
In order to improve the results of treatment of NSCLC
patients, it is necessary to separate a group of patients
with dismal prognosis for whom adjuvant chemotherapy
will permit improving the survival rate [6]. The re-
searches examining the efficacy of adjuvant chemother-

Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer 1473
apy for NSCLC patients are not sufficient. These re-
searches randomize about 8000 of patients during median
term of monitoring of 5 years. By comparison, the re-
search of adjuvant chemotherapy for breast cancer pa-
tients randomized over 100,000 patients during the term
of monitoring for 15 years [3]. In this way it is obligatory
to continue the efficiency of adjuvant chemotherapy for
NSCLC patients. It has been stated that clinical staging is
not appropriate for prognostication of risks of disease
progression for early NSCLC patients [7]. Molecular
markers may help separate a group of patients with dis-
mal prognosis. The studies indicate that molecular mark-
ers may be useful for forecasting of the results of treat-
ment and general survival rate of NSCLC patients [4,
8-11]. At present, a large number of markers are offered
for NSCLC patients. However, none of them found its
place in clinical practice [7]. Further studies of molecular
predictive markers are required. Until then it is not ad-
vised to use them in routine practice [3].
Artificial neural networks have been used in a number
of different ways in medicine and medically related
fields. We have applied ANN to calculate the risk of
NSCLC.
The aim of our research was to create a forecasting
model with a view to detecting the patients with early
NSCLC and dismal prognosis.
2. Materials and Methods
Our research covered 254 patients with the early stage of
NSCLC who underwent a cure from June 2008 till De-
cember 2012 in the department of thoracic surgery of
Zaporizhzhia Regional Clinical oncology dispensary. In
order to identify the factors connected with the risks of
low survival rate of patients with the early stage of
NSCLC after curative treatment (surgical treatment, ad-
juvant chemotherapy), a method of design of neural net-
work models of classification was used [5]. Binary vari-
able Y represents the two outcomes: the survival rate of
less than 12 month was considered low (Y = 1, totally 29
cases); the survival rate of more than 12 months was
considered as a positive result (Y = 0, totally 195 cases).
The censored data for the period of monitoring of less
than 12 months were excluded from the analysis.
Data were collected and analyzed using MedCalc ver-
sion 12.3 statistical software (MedCalc Software Inc,
Broekstraat, Belgium) and STATISTICA Neural Net-
works version 4.0 C (StatSoft Inc., 1999).
39 factors were taken for input characteristics: gender,
age, height, weight, body mass index (BMI), BMI (cate-
gorized index), localization of the tumor and its clinical
form (lung, lobes of the lung, central/peripheral cancer),
neoplasm size, neoplasm size (categorized index), “Т”
criterion, “N” criterion, stage of disease, histological and
morphological form of tumor differentiation, presence of
tumor necrosis, visceral pleura infiltration, volume of
conducted surgical measures, volume of lymph dissec-
tion, fact of intra pericardial lung vasoligation, conduct-
ing of adjuvant radiotherapy and chemotherapy, detec-
tion of lung cancer on prophylactic examination, Кі-67
expression, Кі-67 expression (categorized index), CD31/
CD34 expression, Her2-neu expression, EGFR expres-
sion, p53 expression, Е-cadherin expression, pan-cyto-
keratin expression.
To prevent the re-education and to examine the quality
of forecasting model all cases (using a random number
generator) were divided into 3 sets: instructive (144 cases
used to educate the model), testing (30 cases used to
control the re-education of the model) and a confirming
set (30 cases-used to assess the mistakes of model gener-
alization).
3. Results
The classification model on the full set (39 factors) of
explanatory variables was built. The model was adequate:
area under the ROC (Receiver Operating Characteristic)
curve [12]; AUC = 0.82 ± 0.05. On the training set the
model sensitivity was 94.4% (95% CI 78.1% - 100%)
specificity of the model-91.3% (95% CI 85.7% - 95.6%).
However, the prognostic characteristics on the test set
dramatically decreased (p < 0.05): the model sensitivity
was 28.6% (95% CI 1.0% - 73.2%), specificity of the
model-67.4% (95% CI 52.4% - 80.8%). This reduction in
prognostic model characteristics can be attributed to the
redundancy of factors.
To reveal factors most associated with the risk of low
patient survival rate in the early stage of NSCLC. Selec-
tion of the most important characteristics based on Ge-
netic Algorithm (GA) input selection [13]. Two sets of
explanatory variables were allocated as a result of the
analysis: Model 1 (the patient’s BMI, stage of disease,
histological form of tumor, volume of conducted surgical
measures), Model 2 (the patient’s BMI, volume of con-
ducted lymph dissection, EGFR expression).
The forecasting model of the risk of low patient sur-
vival rate in the early stage of NSCLC was built on the
marked set of factor signs in the Model 1. Its sensitivity
on the training set was 77.8% (95% CI 54.6% - 94.2%),
specificity—72.2% (95% CI 64.0% - 79.7%), the model
sensitivity on the test set was 57.1% (95% CI 14.8% -
93.8%), specificity—55.8% (95% CI 40.5% - 70.6%).
The sensitivity and specificity on the training and test
sets do not statistically differ (p = 0.59 and p = 0.07 ac-
cordingly) which indicates the adequacy of the developed
model.
For revealing the force and direction of the impact of 4
selected explanatory variables a logistic prognostic mo-
del was constructed [14]. Values of the coefficients of
this model are given in Table 1.
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Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer
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Table 1. Coefficients of 4-factor forecasting model of the risk of low patient survival rate.
Factor The value of forecasting model coefficients, b ± mLevel of significance difference, p Odds ratio (OR) (95% CI OR)
The patient’s BMI −0.12 ± 0.05 0.03 0.89 (0.80 - 0.99)
Stage of disease 0.04 ± 0.20 0.82 –
Histological form of tumor −1.00 ± 0.45 0.02 0.36 (0.15 - 0.88)
Type of conducted surgery 1.16 ± 0.46 0.01 3.19 (1.29 - 7.86)
*-statistically significant, p < 0.05.
As follows from the analysis (Table 1) with the in-
creasing of the patient’s BMI the risk of low patient sur-
vival rate statistically significant (p = 0.03) decreases,
odds ratio (OR) = 0.89 (95% CI 0.80 - 0.99) for each
kg/m2 index value. The risk of low patient survival rate
also decreases (p = 0.02) if he has a squamous cell car-
cinoma, OR = 0.36 (95% CI 0.15 - 0.88) compared with
other histological forms of tumor. The connection be-
tween the risk of low patient survival rate and the volume
of surgical intervention was discovered (p = 0.01), OR =
3.19 (95% CI 1.29 - 7.86) for patients who underwent a
pulmonectomy compared with patients who underwent an
upper bilobectomy.
Thus, based on this forecasting model, the most fa-
vorable prognosis was determined in patients with high
BMI, squamous cell lung cancer, conducted surgery in the
volume of lobectomy. Accordingly, the unfavorable
prognosis was determined in patients with low BMI, non-
squamous cell lung cancer, conducted surgery in volume
of pulmonectomy.
The forecasting model of the risk of low patient sur-
vival rate in the early-stage of NSCLC was built on the
marked set of factor signs in the Model 2. Its sensitivity
on the training set was 66.7% (95% CI 42.2% - 87.1%),
specificity—69.8% (95% CI 61.5% - 77.6%). The model
sensitivity on the test set was 42.9% (95% CI 6.2% -
85.2%), specificity—67.4% (95% CI 52.4% - 80.8%).
The sensitivity and specificity on the training and test
sets do not statistically differ (p = 0.53 and p = 0.92 ac-
cordingly) which indicates the adequacy of the developed
model.
For revealing the force and direction of the impact of 3
selected factors a logistic forecasting model was con-
structed [14]. Values of the coefficients of this model are
given in Table 2.
As follows from the analysis (Table 2) with the in-
creasing of the patient’s BMI the risk of low patient sur-
vival rate statistically significant (p = 0.01) decreases; OR
= 0.84 (95% CI 0.74 - 0.96) for each kg/m2 index value.
It is found that with the increasing level of EGFR the
expression in the primary tumor the risk of low patient
survival rate statistically significant increases (p = 0.04),
OR = 1.39 (95% CI 1.01 - 1.90) for each graduation rate.
The risk of low patient survival rate also increases when
conducting the lymph dissection in the volume D0 - D1.
That is the high BMI, low level of EGFR expression,
and D2 lymph dissection are favorable prognostic factors.
Accordingly, the low BMI, high EGFR expression, and
D0 - D1 lymph dissection are unfavorable factors.
Comparison of ROC curves test has been used to
compare the prognostic characteristics of the models
Model 1 and Model 2 for the prediction of the risk of low
patient survival rate in the early-stage of NSCLC. Figure
1 shows the ROC-curves of the models.
The area under the ROC-curve for Model 1: AUC =
0.72 ± 0.05 statistically significant (p < 0.001) differs
from 0.5 and indicating the importance of selected vari-
ables. The area under the ROC-curve for Model 2: AUC =
0.70 ± 0.05 statistically significant (p < 0.001) differs
from 0.5 and indicating the importance of selected vari-
ables too. There is no significant difference between two
AUC (p = 0.79).
Therefore, the conducted analysis shows that the risk
of low patient survival rate with the early-stage of
NSCLC can obtain adequate prediction based on the
analysis of patient’s BMI, histological structure of the
tumor, volume of conducted surgery or patient’s BMI,
volume of conducted lymph dissection, and the level of
EGFR expression.
In both models the patient’s BMI is an important
prognostic parameter. Moreover, the survival rate worsens
with a decrease of this parameter and patients with low
BMI have unfavorable prognosis. Except BMI, the his-
tological structure of tumor has the important prognostic
value. In our study the non-squamous form of the tumor
was prognostic unfavorable. Perhaps this is due to the fact
that this version includes malignant neoplasms of glan-
dular structure, mixt-forms and undifferentiated tumors.
Another important factor in prediction of disease outcome
in patients with the early-stage of NSCLC is the stage of
the disease. This fact is not new but confirms the correct
selection of explanatory variables and the proper staging
in the performance of the study. It is marked that the
prognosis deteriorates for examined patients with in-
creasing stage. The type of conducted operative interfere-
ence and the volume of executed lymph dissection

Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer 1475
Table 2. Coefficients of 3-factor forecasting model of the risk of low patient survival rate.
Factor The value of forecasting model
coefficients, b ± m Level of significance difference, p Odds ratio (OR) (95% CI OR)
The patient’s BMI –0.17 ± 0.07 0.01* 0.84 (0.74 - 0.96)
The volume of conducted lymph dissection –1.05 ± 0.60 0.08 –
EGFR expression 0.33 ± 0.16 0.04* 1.39 (1.01 - 1.90)
*-statistically significant, p < 0.05.
Figure 1. ROC-curves of the models of the risk of low patient
survival rate: Mod el 1 (1), Mod el 2 ( 2), –“O” marked optimal
values of sensitivity and specificity of the model.
showed the important prognostic value. In patients who
underwent the pneumonectomy the deterioration of sur-
vival rate was revealed. Perhaps, due to the large spread of
tumor at the time of the intervention. At the same time the
greater volume of lymph dissection significantly improves
the survival rate of patients with the early stage of NSCLC
that is possible due to the removal of micro metastases and
more correct staging of disease. Of the examined mo-
lecular markers the EGFR expression was prognostic
significant. This marker indicates the presence of epi-
dermal growth factor on the membrane of tumor cells. The
higher number of receptors-the less favorable prognosis of
patients examined. This is because of the fact that the
work of this receptor is associated with many important
processes occurring in the cell, including the increase of
proliferation, apoptosis, cell adhesion, increase of metas-
tatic potential of the tumor. Using these prognostic models
it is possible to select patients with a poor prognosis for
conducting the chemotherapy (PCT).
4. Discussion
A number of studies are devoted to find the prognostic
factors [4,8-11]. The analyzed clinical, morphological,
genetic factors to predict the survival rate of patients with
NSCLC were done. To predict the survival rate Leonar-
dus and coauthors (2010) used a model that included the
following clinical factors: sex, age, smoking, function of
external breathing, comorbidities, stage, type of resection,
histology. It is shown that the forecasting model ade-
quately predicts 1, 2 year survival rate [15]. The follow-
ing clinical factors were also analyzed in our study: sex,
age, height, weight, body mass index (BMI), BMI (cate-
gorized index), localization of the tumor and its clinical
form (lung, lobes of the lung, central/peripheral cancer),
neoplasm size, neoplasm size (categorized index), “T”
criterion, “N” criterion, stage of disease. However, only
the BMI was prognostic significant. Harpole and coau-
thors (1995) showed in their study of 1928 patients that
in the prognostic correlation of 16 analyzed factors (sex,
hemoptysis, cough, chest pain, age, smoking, dyspnea,
type of surgical treatment, histology, tumor size, differ-
entiation, vascular invasion, Her-2/neu, p53, Ki-67 ex-
pression), the most important are the Her-2/neu status,
tumor size more than 3 cm, vascular invasion, p53 status,
low differentiation of the tumor [9]. We also investigated
the prognostic significance of such molecular factors as
histological form and tumor differentiation, Her-2/neu,
p53, Ki-67 expression. None of these prognostic factors
in the forecasting models is important. Perhaps, it is be-
cause of a different set of factors analyzed. In another
study 60 morphological factors in 300 patients with ad-
vanced NSCLC were examined before the first-line
chemotherapy. Prediction and prognostic markers are as
follows: general status of patients, extra pulmonary dis-
tant metastases, type of chemotherapy, number of leuko-
cytes in the blood, level of albumin CYFRA 21-1, nu-
cleosome, Ca125, Ca15-3 and Ca72-4 [10]. In our study
39 prognostic markers were investigated in 254 patients.
It is noted that the independent prognostic factors in the
forecasting model are: the BMI, histological type of tu-
mor, stage, type of surgery which had been conducted.
The possible alternative combination of factors for pre-
dicting the survival rate is as follows: the BMI, volume
of lymph dissection, EGFR expression.
Another important factor in the opinion of Park and
coauthors (2011) is the presence of necrosis in the pri-
mary tumor. It is noted that the tumor necrosis is an in-
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Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer 1476
dependent prognostic factor. Thus, the five-year survival
rate of patients with absence of necrosis was 94.8% and
86.2% in patients with necrosis (p = 0.04). Five-year
recurrence-free survival rate in the absence of necrosis
was 92.1% and in patients with tumor necrosis–78.9% (p
= 0.016) [4]. In our study the tumor invasion in visceral
pleura was investigated (except the presence of necrosis
in the primary tumor). None of these factors was associ-
ated with survival rate of patients.
One of the most important properties of the tumor is
proliferation controlled by a group of cyclines. In their
study (2001) Dosaka-Akita H. and coauthors noted the
different prognostic role of cycline D1 and cycline E.
The cycline E correlated with the index of proliferation
(measured by Ki-67). The cycline E correlated with the
survival rate [16]. According to the data of Lu and coau-
thors (2004) DAPK methylation and the low IL-10 ex-
pression are markers of the negative prognosis for pa-
tients [8]. Rubio L. and coauthors (2005) investigated the
prognostic role of clinical factors such as factor 8 (mi-
crovascular density), VEGF, iNOS, p53, p21. The sig-
nificant prognostic factors were: tumor size (p = 0.0063),
angiogenesis (p = 0.0271) and p21 (p = 0.0478). The
authors indicate that the developed prognostic Cox-
model allows more careful identifying of the patients
with a high risk of disease progression [17].
Among the molecular markers the following markers
were studied for constructing the prognostic model:
erb-b2, р53, bcl2, Ki-67, Cd-44. The multivariate analy-
sis shows the prognostic role of the following factors:
apoptosis (p53), angiogenesis (factor 8), growth factors
(erb-b2), adhesion (Cd-44) and the regulation of the cell
cycle (recessive gene retinoblastoma) [2,14]. At the same
time Hilbe and coauthors (2003) in the study of the
prognostic role of growth factors (EGFR or c-erbb-1,
c-erbb-2 and c-erbb-3), metastases inhibition parameters
(Cd82), markers of proliferation (Ki67, p120) and mark-
ers of apoptosis (p53, bcl-2) did not reveal any relation to
the survival rate of any marker. However, when combin-
ing the factors and survival analysis of patients with high
expression of two or three factors (c-erbb-3, p53, and
micro vascular density) noted significant deterioration of
survival rate [6]. In our research, among the investigated
molecular markers were: Ki-67 expression, Ki-67 ex-
pression (categorized index), CD31/CD34 expression,
Her2-neu expression, EGFR expression, p53 expression,
E-kadherinu expression, expression of pan-cytokeratin.
However, only the EGFR expression in the Model 2
plays an important prognostic role. The use of the PCR
reaction allows analyzing the prognostic significance of
various genes. Thus, Raz and coauthors (2008) analyzed
the genes Wnt3a, Erbb3, LSK and Rnd3. All the patients
were divided into two groups: patients with high and low
risk of disease progression. Five-year survival rate had
41% of the patients with the high risk progression and
62% of the patients with the low risk of it [7].
5. Conclusion
In connection with the uncertainty of the “ideal” predic-
tion factors and their combinations, further large studies
are necessary to be done [8,14]. Some authors have
shown that the use of markers combinations is more
beneficial and prognostic than the use of one of them
[16].
Therefore, the models developed by us include a num-
ber of significant predicting factors that can be used to
predict unfavorable survival rate for patients with the
early-stage of NSCLC which will help identify patients
for further treatment after the operation.
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Abbreviations
ANN—Artificial Neural Network
AUC—Area under the Curve
BMI—Body Mass Index
CI—Confidence Interval
CYFRA—Serum Cytokeratin Fragment
DAPK—Death-Associated Protein Kinase
EGFR—Epidermal Growth Factor Receptor
GA—Genetic Algorithm
IL—Interleukin
iNOS—Inducible Nitric Oxide Synthase
NSCLC—Non-Small Cell Lung Cancer
OR—Odds Ratio
PCR—Polymerase Chain Reaction
PCT—Prognosis for Conducting the Chemotherapy
ROC—Receiver Operating Characteristic
VEGF—Vascular Endothelial Growth Factor