Projectdetails

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What factors are related to bronchial wall thickening on low-dose CT in a general Dutch population?

Keywords:
COPD ageing lungs population screening

Researchers:
dr. R. Vliegenhart
dr. G.J. Pelgrim
drs. I. Dudurych

Nature of the research:
Retrospective Evaluation

Fields of study:
pulmonology radiology

Background / introduction
Lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease (COPD) are called the “big 3” diseases. The “big 3” are on the rise in developed countries, contributing to an increasing health and economic burden. Early detection of respiratory illness may help reduce the development of disease and improve population health. For accurate assessment of airway changes, reference imaging biomarkers require establishment. As part of this study, artificial intelligence (AI) methods have been developed to automatically quantify bronchial parameters such as the airway luminal area and percentage wall area. These tools can be used on a subset of scans from the ImaLife (Imaging in Lifelines) study to measure and compare bronchial parameters. The subset of scans includes 200 participants who have had two scans an average of 3 months apart. This unique dataset allows for investigation into the short-term changes in bronchial parameters of an individual and of groups. Next, the AI software will be used on a larger sample of ImaLife. Potential risk factors derived from the Lifelines database will be related to the bronchial wall thickness.
Research question / problem definition
This study’s goal is to determine whether bronchial parameters of individuals differ on short-term follow-up CT and which factors are related to bronchial wall thickness.
Workplan
Bronchial parameters from initial and repeat scans of 200 participants will be available for this topic. The student will first familiarize themselves with the topic by studying the literature. To investigate the research question, the student will analyse the dataset. The student will learn the basics of chest CT evaluation and will be able to use the AI software for analysis of bronchial segmentation and wall thickness measurements. The results will be presented in a manuscript/article.
References
Xia C, Rook M, Pelgrim GJ, et al (2019) Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population: rationale and design of the ImaLife (Imaging in Lifelines) Study. Eur J Epidemiol. https://doi.org/10.1007/s10654-019-00519-0
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