Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function

Abstract

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

Trial registration: ClinicalTrials.gov NCT00608764.

Figure 1

Two axial CT slices of lung tissue with nearly identical %LAA, but very different spatial distributions of disease. LAA low attenuation area.

Figure 2

Panels (A) and (B) show the connected components clustering of the LAAs. Panel (C) shows relationship between cluster size and the cumulative distribution function for these two slices, and then the power law exponent D is approximately equal to the negative of the slope from the fitted lines.

Figure 3

Top Row: Two simulated patterns illustrating how the normalized join counts are calculated where the different types of joins are marked with different colored lines intersecting the shared edges. Both patterns have the same number of LAA voxels (red), but one was generated by a homogeneous process (A) while the other was generated using a single multivariate normal distribution (B), hence the large difference in NJC (3% in A vs 11% in B). Bottom Row: Panels (C,D) have the two example CT slices used in Figs. 1 and 2 where the zoomed in boxes show the the LAA-to-LAA joins in yellow. LAA low attenuation area, N normal, NJC normalized join-count.

Figure 4

An example application of the spatial point process model to the example slices used in Figs. 1, 2, 3 where approximate cluster boundaries are marked by the green ellipsoids. ACS average cluster size.

Figure 5

tSNE embeddings of each individual CT scan based on the spatial point process clustering characteristics. The left panel is colored by %LAA on the ${log}_{10}$ scale while the right is colored by visual assessment of centrilobular emphysema severity (CLE). LAA low attenuation area.

Figure 6

Panel (A) shows the p-values, on the $-{log}_{10}$ scale, for each combination of quantitative emphysema metric and clinical characteristic based on the ridge regression results. Panel (B) shows the p-values for likelihood ratio tests for either ACS, visual assessment (VA) of centrilobular emphysema (CLE), or visual assessment of paraseptal emphysema from the linear regression models fit to each clinical outcome. The horizontal dashed line represents $p=0.05$ in both panels. LAA low attenuation area, NJC normalized join-count, ACS average cluster size, FEV${}_1$ forced expiratory volume in one second, FVC forced vital capacity, 6MWD 6-minute walk distance, FRC functional residual capacity, TLC total lung capacity, GT gas trapping.

Figure 7

Absolute value of the point estimates and 95% confidence intervals for the regression coefficients from each combination of quantitative emphysema measure and clinical outcomes of interest. Note that the quantitative emphysema measures were all mean-centered and scaled by their standard deviations, so the values represent the absolute change in the outcome for every standard deviation increase in that measure. LAA low attenuation area, NJC normalized join-count, ACS average cluster size, FEV${}_1$ forced expiratory volume in one second, FVC forced vital capacity, 6MWD 6-minute walk distance, FRC functional residual capacity, TLC total lung capacity, GT gas trapping.