Monitoring pulmonary fibrosis by fusing clinical, physiological, and computed tomography features
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), Conference Proceedings of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 6216–6219
Abstract
Advances in medical imaging and screening tests have made possible the detection and diagnosis of many diseases in their early stages. Those advances have enabled more effective planning, execution, and monitoring of a treatment plan. However, early detection has also resulted in an increase of the number of longitudinal radiographs requested for most patients, thus increasing the risk for potential long-term effects of ionizing radiation exposure and increasing the cost associated with a specific treatment plan. The aim of this paper is to study the associations between clinical measurements and quantitative image features in patients with pulmonary fibrosis. The association between these multi-modal features could be used to more accurately determine the state of the disease and could potentially be used to predict many of the longitudinal image features when CT images are not available. Our results show how textural image features are highly correlated with the severity of fibrosis, how clinical variables can be combined to monitor progression, and how simple blood features can be used to predict statistical image attributes of the lungs.
Abstract sourceThis 2011 EMBC paper examined associations between clinical and physiological measurements and quantitative CT features in people with pulmonary fibrosis, exploring whether these combined measures could help characterize disease severity and progression.
Summary
Jesus J. Caban, Jianhua Yao, Ulas Bagci, and Daniel J. Mollura studied relationships between clinical and physiological measurements and quantitative computed tomography (CT) features in 66 people with a history of pulmonary fibrosis. The paper reports associations between CT texture features and fibrosis severity, as well as correlations between selected clinical measurements and image features. The authors also explored models for estimating certain image statistics from clinical variables when CT images were unavailable. These findings describe statistical associations in the study data; they do not establish prospective clinical performance or readiness for clinical use. Read the paper.
Cite this work
Jesus J. Caban; Jianhua Yao; Ulas Bagci; Daniel J. Mollura (2011). Monitoring pulmonary fibrosis by fusing clinical, physiological, and computed tomography features. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), Conference Proceedings of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 6216–6219 https://doi.org/10.1109/IEMBS.2011.6091535
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