Abstract
Quantitative features that can currently be obtained from medical imaging do not provide a complete picture of Chronic Obstructive Pulmonary Disease (COPD). In this paper, we introduce a novel analytical tool based on persistent homology that extracts quantitative features from chest CT scans to describe the geometric structure of the airways inside the lungs. We show that these new radiomic features stratify COPD patients in agreement with the GOLD guidelines for COPD and can distinguish between inspiratory and expiratory scans. These CT measurements are very different to those currently in use and we demonstrate that they convey significant medical information. The results of this study are a proof of concept that topological methods can enhance the standard methodology to create a finer classification of COPD and increase the possibilities of more personalized treatment.
Highlights
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease, affecting more than 200 million people worldwide
The overall aim of this study was to develop a set of new radiomic features that can distinguish between healthy non-smokers as well as healthy smokers and patients with Chronic Obstructive Pulmonary Disease (COPD)
The following four study participant groups defined by smoking status and spirometry given by the GOLD guidelines[6] were studied: healthy non-smokers and healthy smokers, mild COPD patients, consisting of GOLD stage 1 and moderate COPD patients, consisting of GOLD stage 2 (50% ≤ forced expiratory volume in 1 second (FEV1) < 80% of predicted and FEV1/Forced Vital Capacity (FVC) < 0.70)
Summary
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease, affecting more than 200 million people worldwide. We took advantage of the computational tool of persistent homology[11,12,13] to create topological descriptors which capture the complexity of the lung structure; this enabled computation of a measure of similarity between images Using this approach, our study has introduced a novel set of descriptors computable from a chest CT scan, focusing on characteristics that are very different from those used at present. Of note, these stratification results are better than those obtained by other CT measurements, like the emphysema score, volume of the lumen or airway diameter. We propose that further research that applies this method in prospective, longitudinal studies and interventional trials is justified
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