Chest Computed Tomography and Magnetic Resonance Imaging Texture Measurements of Chronic Obstructive Pulmonary Disease
Abstract
Pulmonary imaging using computed tomography (CT) and magnetic resonance imaging (MRI) provide a method to measure airway and parenchymal pathologic information that cannot be provided using spirometry. Currently, it remains difficult to predict which chronic obstructive pulmonary disease (COPD) patients will worsen using spirometry, which although safe and inexpensive, does not provide small airway information where COPD is believed to initiate. Quantitative CT and MRI measurements provide regional structure and function information but are not included in mortality risk assessments, prognosis, or COPD staging. Therefore, my overarching hypothesis is that CT and MRI ventilation texture measurements combined with machine learning will classify at-risk ex-smokers, as well as predict accelerated lung function decline and mortality in ex-smokers with and without COPD. I first accurately detected the presence of abnormal diffusing capacity in ex-smokers without COPD or CT evidence of emphysema, by quantifying visually unapparent CT textures and applying machine-learning models. Next, using baseline MR imaging textures, I evaluated longitudinal data to predict accelerated lung function decline in ex-smokers across 3-years. I identified a subset of MRI texture features that independently predicted rapid worsening, where the longitudinal changes of these texture features correlated with changes in lung function. Finally, I used baseline CT and MRI texture measurements and accurately predicted 10-year mortality, which is the ultimate patient outcome. The series of studies presented here are among the first to demonstrate the feasibility of predicting clinically-relevant outcomes exclusively using CT and MR imaging textures. In addition, machine-learning models trained on established clinical and demographic measurements were outperformed by models trained only using texture features. Taken together, these results suggest that quantitative imaging measurements provide additional prognostic value and perhaps should be considered as potential biomarkers for early detection of COPD and evaluating disease progression and longitudinal patient outcomes.