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Development of decision rules to identifying patients with colon tumors based on gas discharge visualization

Keywords:

E.G. Yakovleva – Ph.D.(Med.), Associate Professor, Russian national research medical university under the name N.I. Pirogov (Moscow)
K.G. Korotkov – Dr.Sc.(Eng.), Professor, Saint-Petersburg national research university of informational technologies, mechanics and optics
E.D. Fedorov – Dr.Sc.(Med.), Professor, Russian national research medical university under the name N.I. Pirogov (Moscow)
E.V. Ivanova – Dr.Sc.(Med.), Russian national research medical university under the name N.I. Pirogov, Federal Clinic Hospital № 31 (Moscow)
R.V. Plachov – Ph.D.(Med.), Associate Professor, Russian national research medical university under the name N.I. Pirogov (Moscow)
S.S. Belonosov – Ph.D.(Med.), Associate Professor, Russian national research medical university under the name N.I. Pirogov (Moscow)


Neoplasms of the colon and their consequences  quite a serious problem today. Important role in solving this problem play screening methods of diagnosing a disease.The gas discharge visualization(GDV) method is a screening procedure, its use for the diagnosis of tumors of the colon may help identify patients in the early stages of the disease.The purpose of this research is to identify statistically significant parameters of static and dynamic GDV-grams to detect individuals with tumors of the colon, as well as to assess the possibility of differential diagnosis of tumors of the colon with the help of gas discharge visualization.With static GDV-survey of 137 people was conducted, with the help of dynamic GDV-graphy - 118 people. Selection of the most informative GDV parameters for separation of thecontrol group and patients with the colon tumors was performed using the Student's t-test (p <0.05) and ROC-analysis.To construct the decision rules stepwise discriminant analysis and binary logistic regression were used. The specificity of the resulting discriminant function was 78.2% and sensitivity 76.8%. The specificity of the rules obtained from the binary logistic regression 78.2%, and sensitivity 90.0%.To construct the decision rules stepwise discriminant analysis was used.For the dynamic GDV-graphy the specificity of the obtained decision rules was 75.8%, and sensitivity 82.4%. The results obtained have shown the ability to identify patients with the colon tumors using static and dynamic GDV-graphy, as well as for differential diagnosis of the colon tumors by their morphology, size and quantity. This opens up good prospects for gas discharge visualization implementation as a screening method.
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