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dc.contributor.author Szigeti, Krisztián
dc.contributor.author Szabo T
dc.contributor.author Korom, Csaba
dc.contributor.author Czibak I
dc.contributor.author Horvath I
dc.contributor.author Veres, Dániel
dc.contributor.author Gyöngyi, Zoltán
dc.contributor.author Karlinger, Kinga
dc.contributor.author Bergmann R
dc.contributor.author Pocsik M
dc.contributor.author Budán, Ferenc Csaba
dc.contributor.author Máthé, Domokos
dc.date.accessioned 2018-09-18T09:13:45Z
dc.date.available 2018-09-18T09:13:45Z
dc.date.issued 2016
dc.identifier 84957708144
dc.identifier.citation pagination=14, pages: 10; journalVolume=16; journalIssueNumber=1; journalTitle=BMC MEDICAL IMAGING;
dc.identifier.uri http://repo.lib.semmelweis.hu//handle/123456789/5951
dc.identifier.uri doi:10.1186/s12880-016-0118-z
dc.description.abstract BACKGROUND: Lung diseases (resulting from air pollution) require a widely accessible method for risk estimation and early diagnosis to ensure proper and responsive treatment. Radiomics-based fractal dimension analysis of X-ray computed tomography attenuation patterns in chest voxels of mice exposed to different air polluting agents was performed to model early stages of disease and establish differential diagnosis. METHODS: To model different types of air pollution, BALBc/ByJ mouse groups were exposed to cigarette smoke combined with ozone, sulphur dioxide gas and a control group was established. Two weeks after exposure, the frequency distributions of image voxel attenuation data were evaluated. Specific cut-off ranges were defined to group voxels by attenuation. Cut-off ranges were binarized and their spatial pattern was associated with calculated fractal dimension, then abstracted by the fractal dimension -- cut-off range mathematical function. Nonparametric Kruskal-Wallis (KW) and Mann-Whitney post hoc (MWph) tests were used. RESULTS: Each cut-off range versus fractal dimension function plot was found to contain two distinctive Gaussian curves. The ratios of the Gaussian curve parameters are considerably significant and are statistically distinguishable within the three exposure groups. CONCLUSIONS: A new radiomics evaluation method was established based on analysis of the fractal dimension of chest X-ray computed tomography data segments. The specific attenuation patterns calculated utilizing our method may diagnose and monitor certain lung diseases, such as chronic obstructive pulmonary disease (COPD), asthma, tuberculosis or lung carcinomas.
dc.relation.ispartof urn:issn:1471-2342
dc.title Radiomics-based differentiation of lung disease models generated by polluted air based on X-ray computed tomography data
dc.type Journal Article
dc.date.updated 2018-07-19T11:35:16Z
dc.language.rfc3066 en
dc.identifier.mtmt 3018923
dc.identifier.wos 000369897900001
dc.identifier.pubmed 26864653
dc.contributor.department SE/AOK/I/Biofizikai és Sugárbiológiai Intézet
dc.contributor.department SE/AOK/K/Radiológiai és Onkoterápiás Klinika
dc.contributor.institution Semmelweis Egyetem


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