Lung cancer
is the most common cause of death from oncological diseases all over the world.
Primary treatment of patients with the early stage of non-small cell lung
cancer is a surgery. However, after surgery 30% - 85% of patients undergo
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 analysis 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 pulmonectomy compared with
patients who underwent an upper bilobectomy. As follows from the analysis
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