Although there are more women than men dying of chronic obstructive pulmonary disease (COPD) in the United States and elsewhere, we still do not have a clear understanding of the differences in the pathophysiology of airflow obstruction between the sexes. Optical coherence tomography (OCT) is an emerging imaging technology that has the capability of imaging small bronchioles with resolution approaching histology. Therefore, our objective was to compare OCT-derived airway wall measurements between males and females matched for lung size and in anatomically matched small airways. Subjects 50-80 yrs were enrolled in the British Columbia Lung Health Study and underwent OCT and spirometry. OCT was performed using a 1.5mm diameter probe/sheath in anatomically matched airways for males and females; the right lower lobe (RB8 or RB9) or left lower lobe (LB8 or LB9) during end-expiration. OCT airway wall area (Aaw) was obtained by manual segmentation. For males and females there was no significant difference in OCT Aaw (p=0.12). Spearman correlation coefficients indicated that the forced expiratory volume in 1 second (FEV1) and Aaw were significantly correlated for males (r=-0.78, p=0.004) but not for females (r=-0.20, p=0.49) matched for lung size. These novel OCT findings demonstrate that while there were no overall sex differences in airway wall thickness, the relationship between lung function and airway wall thickness was correlated only in men. Therefore, factors other than airway remodeling may be driving COPD pathogenesis in women and OCT may provide important information for investigating airway remodeling and its relationship with COPD progression.
The objective was to develop an automated optical coherence tomography (OCT) segmentation method. We evaluated three ex-vivo porcine airway specimens; six non-sequential OCT images were selected from each airway specimen. Histology was also performed for each airway and histology images were co-registered to OCT images for comparison. Manual segmentation of the airway luminal area, mucosa area, submucosa area and the outer airway wall area were performed for histology and OCT images. Automated segmentation of OCT images employed a despecking filter for pre-processing, a hessian-based filter for lumen and outer airway wall area segmentation, and K-means clustering for mucosa and submucosa area segmentation. Bland-Altman analysis indicated that there was very little bias between automated OCT segmentation and histology measurements for the airway lumen area (bias=-6%, 95% CI=-21%-8%), mucosa area, (bias=-4%, 95% CI=-14%-5%), submucosa area (bias=7%, 95% CI=-7%-20%) and outer airway wall area segmentation results (bias=-5%, 95% CI=-14%-5%). We also compared automated and manual OCT segmentation and Bland-Altman analysis indicated that there was negligible bias between luminal area (bias=4%, 95% CI=1%-8%), mucosa area (bias=-3%, 95% CI=-6%-1%), submucosa area (bias=-2%, 95% CI=-10%-6%) and the outer airway wall (bias=-3%, 95% CI=-13%-6%). The automated segmentation method for OCT airway imaging developed here allows for accurate and precise segmentation of the airway wall components, suggesting that translation of this method to in vivo human airway analysis would allow for longitudinal and serial studies.