HC received $4800 in the years 2008–2012 for serving on the steering committee for the ECLIPSE project for GSK. In addition, HC was the co-investigator on two multi-center studies sponsored by GSK and has received travel expenses to attend meetings related to the project. HC has three contract service agreements with GSK to quantify the CT scans in subjects with COPD and a service agreement with Spiration Inc. to measure changes in lung volume in subjects with severe emphysema. HC was the recipient of a GSK Clinical Scientist Award in 2010. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: AMDL CM JE RF SL HC PL. Performed the experiments: AMDL KO TC RS PL. Analyzed the data: AMDL MK TC RS PL. Contributed reagents/materials/analysis tools: AMDL MK PL. Wrote the paper: AMDL MK PL. Revised manuscript: AMDL MK CM JE RF SL HC PL. Gave final approval for manuscript: AMDL MK KO TC RS CM JE RF SL HC PL.
Examining and quantifying changes in airway morphology is critical for studying longitudinal pathogenesis and interventions in diseases such as chronic obstructive pulmonary disease and asthma. Here we present fiber-optic optical coherence tomography (OCT) as a nondestructive technique to precisely and accurately measure the 2-dimensional cross-sectional areas of airway wall substructure divided into the mucosa (WAmuc), submucosa (WAsub), cartilage (WAcart), and the airway total wall area (WAt). Porcine lung airway specimens were dissected from freshly resected lung lobes (N = 10). Three-dimensional OCT imaging using a fiber-optic rotary-pullback probe was performed immediately on airways greater than 0.9 mm in diameter on the fresh airway specimens and subsequently on the same specimens post-formalin-fixation. The fixed specimens were serially sectioned and stained with H&E. OCT images carefully matched to selected sections stained with Movat’s pentachrome demonstrated that OCT effectively identifies airway epithelium, lamina propria, and cartilage. Selected H&E sections were digitally scanned and airway total wall areas were measured. Traced measurements of WAmuc, WAsub, WAcart, and WAt from OCT images of fresh specimens by two independent observers found there were no significant differences (p>0.05) between the observer’s measurements. The same wall area measurements from OCT images of formalin-fixed specimens found no significant differences for WAsub, WAcart and WAt, and a small but significant difference for WAmuc. Bland-Altman analysis indicated there were negligible biases between the observers for OCT wall area measurements in both fresh and formalin-fixed specimens. Bland-Altman analysis also indicated there was negligible bias between histology and OCT wall area measurements for both fresh and formalin-fixed specimens. We believe this study sets the groundwork for quantitatively monitoring pathogenesis and interventions in the airways using OCT.
Airway wall remodelling is a key component of chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and asthma. Historically, COPD and asthma have been studied using surgical specimens and bronchial biopsies, with histopathology considered the ‘gold-standard’ for quantifying airway dimensions. While producing very valuable information, histology has a few key limitations. Histology requires the removal of tissue and is therefore limited in sampling, both in terms of the number of available samples for cross-sectional studies and in the application of the technique for longitudinal and interventional studies. Specimen shrinkage and distortion during histological processing steps also limit the accuracy of quantitative measurements
Optical coherence tomography (OCT) is a relatively new imaging technique that can potentially overcome many of the aforementioned limitations for
Fiber optic OCT probes have also been developed for endoscopic use that have allowed
With respect to evaluating airway remodelling in chronic lung diseases, there have been limited studies validating airway wall areas measured with OCT and comparing them with those from precisely matched histology. Here we present a study of ex vivo porcine airways and our objectives were to: 1) identify the sub-surface components of the airway wall relevant to airway disease, 2) measure OCT airway wall components and compare these measurements to the gold standard (histology), and, 3) evaluate the inter-observer reproducibility of OCT airway wall component measurements. Demonstrating these capabilities in an
Preparation of the
A) Experimental procedure for airway preparation and OCT imaging. B) Diagram outlining matching of H&E and OCT images, manual tracing of the morphological perimeters, and calculation of the airway wall area components. Pp = probe perimeter, Pi = luminal perimeter, Pmi = muscle inner perimeter, Pci = cartilage inner perimeter, Po = airway outer boundary perimeter. WAmuc = mucosal wall area, WAsub = submucosal wall area, WAcart = cartilage wall area. Scale bar = 1 mm.
The airway specimens were rinsed with phosphate buffered saline (PBS) solution to remove mucous and blood. The entire fresh airway specimens were imaged while submerged under PBS to minimize probe-air and tissue-air interface reflections. Following overnight fixation in 10% formalin solution, the specimens were submerged in fresh 10% formalin solution and OCT imaged for a second time. The specimens were then dehydrated using standard histological procedures. Due to the dimensions of the embedding trays, airway specimens were cut into segments of 1 cm or shorter. Photographs and additional ink markers ensured correct ordering and orientation of the multiple airway segments prior to embedding into paraffin blocks. Selected blocks were serially sectioned with 200 um to 300 um spacing between adjacent sections and stained with haematoxylin and eosin (H&E). Two of the blocks were sectioned such that 2 slides were collected every 300 um. One slide from each of the 2-slide sets was stained with H&E. The second slide from the set was stained with Movat’s pentachrome to further differentiate tissue components. All slides were scanned at 20X using a Panoramic MIDI slide scanner (3DHistech Kft., Hungary).
The donation of animal tissue was approved by the institutional review board at the University of British Columbia (A11-0374).
A Lightlabs C7XR (St. Jude Medical, St. Paul, MN) swept-source OCT system was used for imaging. The specimens were imaged using 0.9 mm diameter rotary-pullback C7 Dragonfly catheters (St. Jude Medical, St. Paul, MN). Light exits the probe in the forward direction at an angle 68.5° relative to the probe axis in air. This system is centred around 1310 nm and provides axial resolution of approximately 15 µm and a section thickness of 5 µm. Three-dimensional volumetric images were acquired at 100 Hz frame rate and 2 to 5 mm/s pullback rate, providing an imaging pitch of 20–50 µm between frames.
The procedures for registering OCT and histology images and airway wall measurements is shown in
For each fresh and formalin-fixed OCT image in the matched sets, the following perimeters were traced using ImageJ software (NIH, Bestheda, MD) (abbreviations derived from
Analogously to the OCT perimeters, Pi, Pmi, Pci, and Po, were traced on the H&E stained histology images by a single observer (Observer 1). As artifacts during tissue processing creates considerable white space or voids in the histological slides especially adjacent to the airway cartilage, Pci was traced to lie approximately halfway between the cartilage inner boundary and the submucosal outer boundary. The areas enclosed by the perimeters were used to generate the corresponding areas Ai, Ami, Aci, and Ao. Wall areas WAmuc, WAsub, WAcart, and WAt were calculated as for the OCT measurements. Additionally, image processing was used to mask out the white space from area (Ami, Aci, and Ao) and all wall area measurements to yield the quantities Ami′, Aci′, Ao′, WAmuc′, WAsub′, WAcart′, and WAt′.
A paired two-tailed t-test with Holm-Bonferroni correction for multiple comparisons was performed for statistical comparison between the two observer’s OCT measurements using GraphPad Prism version 4.00 (GraphPad Software Inc, San Diego, CA, USA). The relationship between the two observers OCT measurements was determined using Pearson correlation coefficients (r); Bland-Altman analysis was also used to determine the agreement between observers OCT measurements using GraphPad Prism version 4.00. OCT measurement inter-observer reproducibility was calculated using the coefficient of variation (CV); CV was calculated as the pooled standard deviation (SD) of the two observers measurements divided by the pooled mean of the two observers measurements and multiplying the result by 100.
Airway wall areas (WAmuc, WAsub, WAcart, and WAt) measured with 1) OCT on fresh specimens; 2) OCT on post-formalin fixed specimens; and 3) histology were compared pair-wise using a linear regression model with least squares optimization. The OCT measurements were pooled from both observers for the linear regression and Bland-Altman analysis.
A total of 60 H&E slide images and formalin-fixed OCT images from 10 different airway blocks were used for quantitative measurement of the airway total wall area.
An example matched set of fresh and post-formalin fixed OCT and H&E histology images are shown in
A, D) Fresh OCT images, B, E) H&E and Movat’s pentachrome photomicrographs respectively, C, F) Post-formalin fixed OCT images. Tracings: green = Po, yellow = Pci, blue = Pmi, red = Pi, white = Pp. in B) E = epithelium, LP = lamina propria, SM = smooth muscle, C = cartilage.
As shown in
A. There was a significant correlation between observer 1 and 2t (r2 = 0.98, p<0.0001), WAmuc (r2 = 0.89, p<0.0001), WAsub (r2 = 0.94, p<0.0001) and WAcart (r2 = 0.84, p<0.0001). B. Bland-Altman analysis indicates a small bias between observers for WAt (bias = −0.15±0.40, 95% CI = −0.64–0.95), WAmuc (bias = 0.06±0.14, 95% CI = −0.34–0.23), WAsub (bias = 0.14±0.26, 95% CI = −0.37–0.66), WAcart (bias = −0.06±0.50, 95% CI = −0.91–1.03). Solid lines represent the bias and dotted lines represent the 95% confidence intervals.
Obs 1 (N = 10) | Obs 2 (N = 10) | Difference (±SD) | P-value | |
Ai mm2 | 3.12 | 3.09 | −0.03 (0.12) | 1.00 |
Ami mm2 | 4.05 | 4.07 | 0.02 (0.07) | 0.74 |
Aci mm2 | 5.72 | 5.60 | −0.12 (0.14) | 0.14 |
Ao mm2 | 8.51 | 8.32 | −0.19 (0.33) | 0.66 |
WAmuc mm2 | 1.67 | 1.52 | 0.05 (0.11) | 0.72 |
WAsub mm2 | 2.79 | 2.73 | −0.14 (0.15) | 0.08 |
WAcart mm2 | 5.39 | 5.24 | −0.06 (0.39) | 0.63 |
WAt mm2 | 0.93 | 0.99 | −0.15 (0.32) | 0.85 |
Inter-observer reproducibility was moderately high for OCT-fresh measurements (WAt: CV = 5%, 95% CI = 3–8%; WAmuc: CV = 9%, 95% CI = 6–16%; WAsub: CV = 9%, 95% CI = 6–16%; WAcart: CV = 10%, 95% CI = 7–17%). Inter-observer reproducibility for post-formalin OCT measurements is provided in Text S1 in
(A) Correlation plots for measurements of WAmuc, WAsub, WAcart, and WAt by both observers for fresh OCT imaging vs. histology (whitespace included). The solid lines are linear regression fits of the data and dotted lines are the regression fits of the data with the intercept of the model constrained to pass through the origin. (B) Bland-Altman analysis for WAmuc, WAsub, WAcart, and WAt measurements by both observers for fresh OCT imaging vs. histology. Solid lines represent the bias and dotted lines represent the 95% confidence intervals.
Fresh OCT vs. Histology | Post-Formalin OCT vs. Histology | Post-Formalin OCT vs. Fresh OCT | |
WAmuc | 1.30±0.05 (0.93) | 1.21±0.04 (0.94) | 0.90±0.03 (0.94) |
WAsub | 1.40±0.06 (0.92) | 1.13±0.05 (0.89) | 0.78±0.03 (0.92) |
WAcart | 0.79±0.04 (0.89) | 0.91±0.03 (0.95) | 1.08±0.04 (0.93) |
WAt | 1.03±0.02 (0.97) | 1.02±0.02 (0.98) | 0.98±0.02 (0.98) |
WAmuc′ | 1.31±0.05 (0.93) | 1.21±0.04 (0.93) | |
WAsub′ | 1.50±0.06 (0.92) | 1.20±0.06 (0.88) | |
WAcart′ | 0.82±0.04 (0.89) | 0.95±0.03 (0.95) | |
WAt’ | 1.08±0.02 (0.98) | 1.06±0.02 (0.98) |
Wall areas denoted with a prime are for fits against histology wall areas with the whitespace area removed. R2 values are shown in parentheses.
The accurate analysis of airway dimensions is key to understanding chronic airway remodelling and its response to therapy. To date this analysis has been hampered by the destructive nature of histology on resected specimens and the limited resolution of CT scanning. In the current study we present data from a new non-destructive optical imaging technique that allows us to measure airway wall dimensions with micron scale resolution. Our data show that this technique produces images that allow investigators to visualize and measure the airway wall in a reproducible and reliable manner.
Our data show that OCT measurements of wall area between observers are significantly correlated and have negligible bias. There were also no significant differences between observers for the fresh OCT wall area (WAx) or area measurements (Ax). Moderately high inter-observer reproducibility was found for OCT generated wall areas. Each wall area measurement is dependent on the accuracy of the two enclosing perimeters. For WAmuc, the luminal perimeter (Pi) is easily defined as the boundary between the imaging medium and the tissue, however, the delineation of the bottom of the highly scattering lamina propria (Pmi) is more difficult and results in the relatively high value of CV = 9%. For WAsub, the inner cartilage perimeter (Pci) is well imaged as a sharp drop in scattering intensity, but the CV = 9% is primarily due to observer variability of Pmi. The outer boundary of the cartilage (Po) is more difficult to discern in some cases because of the contrast falloff with distance from the probe, resulting in a relatively large CV = 10% for WAcart. When looking at WAt, the relatively low value of CV = 5% results because the Po variability has a smaller effect on the total wall area. Interestingly, as seen in Text S1 in
By closely matching OCT image frames within the 3D data sets with histological sections, we compared airway wall measurements obtained from OCT and histology. Comparing post-formalin OCT imaging with fresh OCT imaging, we find that WAmuc is unaffected (slope = 0.99±0.01), however, WAsub is smaller in post-formalin imaging by a factor of 0.89±0.02 while WAcart is larger by a factor of 1.06±0.02. Overall for the total wall area, WAt, there is no difference (slope = 1.00±0.01) in the measured areas between fresh and post-formalin-fixation OCT imaging. The differential changes seen in WAsub and WAcart due to formalin fixation could be the result of several factors that may be a combination of: 1) the wall area subcomponents swelling or shrinking upon fixation; 2) the OCT imaging tissue contrast changing due to biochemical changes; 3) changing refractive indices of the tissue components.
The OCT wall area measurements, both fresh and post formalin, are significantly correlated with measurements from histology and the slopes of the regression lines reveal some interesting results regarding the swelling-shrinkage of the wall components. Fresh and post-formalin OCT imaging measures of WAt are about the same, however, the airway wall subcomponents appear to be different with cartilage shrinking and the submucosa swelling. Removal of the whitespace from the histology images does change the amount of shrinkage and expansion observed for all wall areas, with the overall shrinkage of the total airway wall area WAt, reduced by a factor of 1.13±0.01 for histology compared to both fresh and post-formalin OCT imaging.
The differences between the OCT and histological measurements are due to either 1) artifacts due to histological processing or 2) OCT imaging artifacts. Histological processing artifacts (primarily the dehydration steps) may be the greater cause of the wall area measurement deviations from their true value for the following reasons. It is well documented that tissue shrinkage occurs during the preparation of histological slides; however, the amount of shrinkage can vary widely depending on the tissue type, and the processing procedure
For airway diseases such as asthma, it would be ideal if the airway smooth muscle (ASM) could be easily visualized and measured. The quantity measured here WAsub contains both the ASM and glands. In these experiments, visualization of ASM by structural OCT imaging alone was variable. Further extensions of OCT such as endoscopic polarization-sensitive OCT
The results of this
However, it is important to note that for longitudinal and serial human studies it will be challenging to identify the same location within the airway tree. Importantly, to better understand how airway remodeling is modified by therapy or to determine how airway remodeling occurs over time during disease progression, the reproducibility of OCT for identifying and evaluating the same airway segment within the airway tree must be established. In this regard, we have previously performed a small pilot study in current or ex-smokers evaluating the OCT scan-rescan insertion reproducibility and demonstrated that insertion reproducibility is high in the middle lobe [25]. However, this is an important consideration for future studies and should be evaluated further in a larger number of subjects.
We must, however, acknowledge that this study is limited by several factors. One limitation of our study was including only two observers in our inter-observer reproducibility analysis. Including more observers would have strengthened our claims that OCT measurements have high inter-observer reproducibility. We also acknowledge that OCT is limited by depth penetrance. Depth penetration in OCT images is an important consideration because there is potential for the depth penetrance within the airway to be insufficient for identifying and measuring the airway wall in subjects with significant airway remodeling. However, it is important to note that a previous study evaluating OCT airway wall thickness in 44 current and ex-smokers with a range of airflow limitation did not report the inability to identify and measure the airway wall
Other techniques have been used to measure airway wall changes in airway diseases. Multi-detector row CT scanners and new airway measurement algorithms have made it possible to obtain measurements of
Summarizing, we have performed OCT imaging on 10 excised porcine airways and matched as close as possible these images with selected histological sections (N = 60) of the same specimens. Measurements by two independent observers showed that there were no significant differences in fresh OCT measurements of the mucosa, submucosa, cartilage, and total wall areas. Comparisons of wall area measurements between OCT and histology showed that OCT measures larger mucosal and submucosal wall areas and smaller cartilage wall areas. The airway total wall area with OCT on fresh and fixed airways were larger than the same measurements on histology. OCT is an important technique for the imaging of human airways and these findings indicate that it may provide valuable information on tissue changes caused by disease and response to intervention.
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The authors would like to acknowledge Dr. Ivana Cecic and Ms. Dorothy Hwang (BCCRC) for technical assistance.