Radiomics-based differentiation of lung disease models generated by polluted air based on X-ray computed tomography data
| Autor: | Ralf Bergmann, Tibor Szabó, Dániel S. Veres, Csaba Korom, Kinga Karlinger, Ildiko Horvath, Domokos Máthé, Ferenc Budán, Zoltán Gyöngyi, Krisztián Szigeti, Ilona Czibak, Márta Pócsik |
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| Rok vydání: | 2016 |
| Předmět: |
Lung Diseases
0301 basic medicine medicine.medical_specialty In vivo micro-CT Normal Distribution Air pollution computer.software_genre Fractal dimension 030218 nuclear medicine & medical imaging Normal distribution Mice 03 medical and health sciences symbols.namesake 0302 clinical medicine Fractal Voxel Image Interpretation Computer-Assisted medicine Gaussian function Animals Humans Radiology Nuclear Medicine and imaging Air Pollutants Mice Inbred BALB C Radiomics business.industry Attenuation Nonparametric statistics Disease Models Animal Fractals 030104 developmental biology Technical Advance Radiology Nuclear Medicine and imaging Lung disease symbols Female Radiology Tomography Tomography X-Ray Computed business Nuclear medicine computer |
| Zdroj: | BMC Medical Imaging 16(2016), 14 BMC Medical Imaging |
| ISSN: | 1471-2342 |
| Popis: | 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. Electronic supplementary material The online version of this article (doi:10.1186/s12880-016-0118-z) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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