Findings on High Resolution Computed Tomography in Symptomatic Veterans with Deployment-Related Lung Disease

Abstract

PURPOSE

Military deployment to dusty, austere environments in Southwest Asia and Afghanistan is associated with symptomatic airways diseases including asthma and bronchiolitis. The utility of chest high resolution computed tomographic (HRCT) imaging in lung disease diagnosis in this population is poorly understood. We investigated visual assessment of HRCT for identifying deployment-related lung disease compared to healthy controls.

MATERIALS AND METHODS

Chest HRCT images from 46 healthy controls and 45 symptomatic deployed military personnel with clinically confirmed asthma and/or biopsy-confirmed distal lung disease were scored by three independent thoracic radiologists. We compared demographic and clinical characteristics and frequency of imaging findings between deployers and controls, and between deployers with asthma and those with biopsy-confirmed distal lung disease, using Chi-Square, Fisher Exact or t-tests, and logistic regression where appropriate. We also analyzed inter-rater agreement for imaging findings.

RESULTS

Expiratory air trapping was the only chest CT imaging finding that was significantly more frequent in deployers compared to controls. None of the 24 deployers with biopsy-confirmed bronchiolitis and/or granulomatous pneumonitis had HRCT findings of inspiratory mosaic attenuation or centrilobular nodularity. Only two of 21 with biopsy-proven emphysema had emphysema on HRCT.

CONCLUSIONS

Compared to surgical lung biopsy, visual assessment of HRCT showed few abnormalities in this small cohort of previously deployed symptomatic veterans with normal or near-normal spirometry.

Introduction

Military deployment to dusty and austere environments in Southwest Asia and Afghanistan has been linked to large and small airways diseases including asthma and bronchiolitis.¹ Bronchiolitis involves inflammation and/or fibrosis of the distal airways less than two mm in diameter. Indolent bronchiolitis is typically patchy and difficult to diagnose with conventional tools and may require tissue confirmation.^2–6 Since surgical lung biopsy is an invasive procedure, noninvasive markers of small airways disease are needed.

In combination with other clinical findings, chest high resolution computed tomography (HRCT) may be helpful in the diagnosis of bronchiolitis. Centrilobular nodules on HRCT may reflect a cellular/inflammatory bronchiolitis, while patchy expiratory air trapping and inspiratory mosaic attenuation may signal obliterative/constrictive bronchiolitis.⁷ However, the reported sensitivity of CT for detection of exposure-related or transplant-related bronchiolitis has been variable.^2,8–10 We assessed the utility of HRCT in the diagnosis of airways abnormalities in those returning from military deployment (“deployers”) with persistent respiratory symptoms who underwent comprehensive clinical evaluation including methacholine challenge to evaluate for asthma and/or surgical lung biopsy to assess for bronchiolitis. An independent panel of thoracic radiologists used a standardized image acquisition protocol and scoring system to assess the presence and extent of HRCT abnormalities in deployers compared to controls. We hypothesized that HRCT would be useful in detecting histologic abnormalities noted on lung biopsy and in distinguishing deployers with large and/or small airways disease from controls. We also analyzed inter-reader variability among study radiologists for imaging findings that typically characterize large and small airways diseases.

Materials and Methods

Study Populations

We conducted a cross-sectional study comparing HRCT image findings from 45 deployers with persistent post-deployment respiratory symptoms to 46 control HRCT images from the COPDGene Study.

Deployers:

With written informed consent (institutional review board name blinded per journal request: HS-2689/HS-3022), we analyzed chest HRCT images from patients [institution blinded per journal request] who were evaluated for persistent respiratory symptoms that began during or after post-9/11 military deployment to Southwest Asia and Afghanistan. Using a standardized questionnaire, we collected information on medical, smoking, and exposure histories including inhalational exposures to desert dust/sandstorms, burn pits/trash-burning emissions, diesel, and combat dust. In addition to HRCT imaging, deployers underwent physical examination, pulmonary function testing (including pre- and post-bronchodilator spirometry, lung volumes, and diffusion capacity for carbon monoxide, DLCO), and methacholine challenge.

Pulmonary function testing was conducted in accordance with American Thoracic Society (ATS) Standards^11–13 and analyzed along with the most closely temporally linked HRCT. Reference values for spirometry, DLCO, and lung volumes were obtained from the Global Lung Function Initiative (GLI) prediction equations.^14–16 Hyperinflation was defined physiologically based on a residual volume >120 percent predicted (PP). Decreased DLCO was defined as <80 PP. A diagnosis of deployment-related asthma (DRA) was made if the patient had either a post-bronchodilator increase in FEV1 or FVC ≥ 12% and ≥ 200 milliliters on pulmonary function testing or airways hyper-responsiveness based on methacholine challenge showing a PC20 FEV1 of ≤ 4 mg/mL (definite DRA) or PC20 FEV1 > 4 and ≤ 16 mg/mL (probable DRA).^17,18

Twenty-four symptomatic deployers underwent diagnostic surgical lung biopsy and were found to have significant histologic abnormalities. Those referred for biopsy had disabling and unexplained respiratory symptoms, with either negative methacholine challenge or failure to improve despite aggressive treatment for asthma. Biopsies were obtained from upper, middle, and lower lobes, mainly from the right lung. Specimens were formalin fixed and paraffin embedded, stained with hematoxylin and eosin, and all lobes were examined and interpreted by an experienced pulmonary pathologist using a research data capture (REDCap)-based evaluation form.¹⁹ Systematic scoring of histologic abnormalities was performed for five lung compartments: small airways, alveolar airspaces, interstitium, vasculature, and pleura.²⁰ Since samples were obtained via surgical lung biopsy, large airways were absent and could not be scored histologically. Deployers who had surgical lung biopsy findings of bronchiolitis, granulomatous pneumonitis, and/or hyperinflation with emphysema were diagnosed with deployment-related distal lung disease (DDLD).¹ Only deployers with findings of deployment-related asthma and/or DDLD were included in this analysis.

Controls:

Control HRCT images on smoking and non-smoking subjects with normal spirometry were obtained with permission from the COPDGene^® Study (ancillary study number ANC246). These smoking and non-smoking controls were selected to be as similar as possible to deployers in terms of age, gender, race/ethnicity, smoking status (ever, current, former), and smoking pack-years. As described previously,^21,22 the non-smoking controls with no history of lung disease were recruited at COPDGene centers around the United States using word-of-mouth communication to friends and spouses of chronic obstructive pulmonary disease (COPD) subjects, advertisements, and outreach to community groups and churches. Participants were of non-Hispanic white or non-Hispanic black race/ethnicity and between the ages of 45-80. All underwent spirometry before and after bronchodilator and DLCO measurement. Participants who met the ATS spirometry definition for asthma (both a 200 cc and a 12% increase in FEV1 after bronchodilator) or self-reported diagnoses of asthma or other lung disease in the last five years were excluded from the current study (n=2).¹⁸

Imaging technique/CT acquisition protocol

Deployer chest HRCT scans were acquired on either a Siemens Sensation 64 or Siemens AS 128 scanner using a standardized protocol, as used in Phase 3 of the COPDGene^© Study.²³ Thin sections (0.5-0.75 mm slice thickness) with a moderately high spatial frequency reconstruction algorithm were used to enhance parenchymal and small airways findings. Volumetric scans were obtained on full inspiration with dose modulation (modulated ref 35 mAs) and at the end of normal expiration (Functional Residual Capacity) (50 mA). A radiology analyst uploaded images in TeraRecon for independent scoring by study radiologists, who were blinded to deployer versus control scan status. The COPDGene control subjects were scanned using the protocol for Phases 1 and 2 of COPDGene. ²²

Image scoring

Contiguous thin sections were reviewed on dedicated Picture Archive and Communication System (PACS) workstations, using standard mediastinal and lung window settings (window width and level 350 and 40 for soft tissue, and 1500 and −700 for lung). Three reviewers evaluated each study. All thoracic radiologists were blinded to subject status and independently scored the images. One study radiologist left the institution before completion of the study and was replaced by a fourth radiologist. All of the study’s board-certified thoracic radiologists were experienced in the evaluation of chest HRCTs (DL >40 years, AO 3 years, JR 7 years, and TK 7 years of academic experience in thoracic radiology). Radiologists used a REDCap-based scoring form that was pilot-tested and modified to focus mainly on large and small airways findings (see Supplement 2, HRCT Image Scoring Form).

Image quality was rated by study radiologists as adequate, suboptimal or inadequate for diagnosis. Only one study was deemed inadequate and excluded from analysis. Variables of interest focused on the presence and extent of airways abnormalities including bronchial wall thickening (absent/mild/moderate/severe), inspiratory mosaic attenuation (absent/mild/moderate/severe), expiratory air trapping (absent/mild/moderate/severe), and centrilobular nodularity (presence/distribution/extent). Findings also were assessed by distribution and type (lobular/geographic/non-lobular/diffuse). Radiologists scored the presence or absence of several additional findings including fibrotic abnormalities (reticulation, traction bronchiectasis, honeycombing), ground glass opacities, other nodularity, emphysema, bronchiectasis, masses or nodules, lymphadenopathy, and pleural disease.

Statistical analysis

Demographic and clinical characteristics of deployer and control study subjects were compared using Chi-square and Fisher Exact tests for categorical variables and two-sided t-tests for continuous variables using the Satterthwaite result with a p-value <0.05 to assess statistical significance. Because group differences in age and smoking pack-years were substantial, we treated the two groups as separate cohorts rather than performing paired analyses.

The mode was used to calculate tie-break scores between readers. In cases where the mode did not exist, discordant readings of absent, mild and moderate were assigned as mild. Similarly, discordant readings of absent, moderate and severe were assigned as moderate. Because of small counts in some groups for all variables scored, tie-break or consensus scores were compared using Fisher Exact tests.

Since two of the study radiologists each scored approximately half of the images, inter-rater agreement was assessed using the MAGREE SAS macro,^24,25 which allows for the estimation of agreement among multiple raters in the presence of missing data. While bronchial wall thickening, inspiratory mosaic attenuation and expiratory air trapping were scored on an ordinal scale, these variables were collapsed to absent vs. present and analyzed in a nominal scale because of small numbers of ratings across severity levels. We calculated Fleiss’ Kappa, which is commonly used to compare agreement between multiple raters, as well as Gwet’s first order agreement coefficient (AC1),²⁶ another statistic used to assess inter-rater reliability between multiple raters which is less sensitive to disease prevalence. When there is low disease prevalence, Kappa statistics can suggest low inter-rater reliability despite high percent agreement among raters; this is sometimes called the “Kappa paradox”.^27,28 Gwet’s AC1 does not have this limitation.^29,30 While the MAGREE macro can compute Gwet’s AC1 and the associated p-value in the presence of missing data, it does not provide p-values for Fleiss’ Kappa. Both AC1 and Fleiss’ Kappa can range from 1 for perfect agreement to <0 for no agreement; other agreement levels include 0-<0.2 = slight, 0.2-<0.4 = fair, 0.4-<0.6 = moderate, 0.6-<0.8 = substantial, 0.8-<1 = almost perfect.

To adjust comparisons between deployers and controls for differences in age and smoking pack-years, we collapsed the consensus scores for bronchial wall thickening and air trapping to create the binary variables of absent vs. present and used logistic regression to evaluate differences in the frequency of these findings. To compare the frequency of bronchial wall thickening among deployers (n=45) with and without deployment-related asthma, we used logistic regression. Fisher exact tests were used to compare the frequency of HRCT findings among deployers with available biopsies (n=24). All analyses were performed in SAS v. 9.4 (SAS Institute Inc., Cary, North Carolina).

Results

Comparative demographic and spirometry findings between the 45 study subjects with deployment-related lung disease and 46 controls are shown in Table 1. Both groups were predominantly male (86%) and white (93%). By design, the two groups were similar in smoking status, with 68% overall having never smoked cigarettes, 25% reporting previous smoking, and 7% current smokers. Deployers had significantly lower total smoking pack-years (p=0.001), with a mean of 20.5 fewer pack-years compared to controls. Deployers also were 19 years younger on average than controls (p<0.0001). Deployers had an average of 1.9 years (range 4 months - 7.2 years) of total deployment to Southwest Asia and/or Afghanistan, and the mean number of deployments was 2.7. Reported inhalational exposures included sandstorms (93%), burn pit combustion products (98%), diesel exhaust (96%), and combat dusts (91%, including blasts from improvised explosive devices, mortar fire, and controlled detonations).

Table 1.

Demographic characteristics and pulmonary function in deployers with deployment-related lung disease and controls

	Deployment-related Lung Disease n=45	Controls n=46	p-value*
Demographic Characteristics
Age (years)	39.4 (± 9.2)	58.3 (± 6.8)	<0.0001
Male	40 (89%)	38 (83%)	0.39
White	41 (91%)	44 (96%)	0.71+
Smoking Status
Never	33 (73%)	29 (63%)	0.53+
Former	10 (22%)	13 (28%)
Current	2 (4%)	4 (9%)
Pack-years	5.2 (± 3.6)	25.8 (± 21.8)	0.001
BMI (kg/m2)	30.4 (± 5.0)	28.0 (± 6.2)	0.05
Pulmonary Function ^
FVCPP	92.6 (± 13.5)	97.1 (± 12.3)	0.10
FEV1PP	91.1 (± 16.9)	94.7 (± 14.8)	0.29
FEV1/FVC ratio	79.4 (± 8.1)	76.1 (± 8.0)	0.06
RVPP	129.0 (± 26.6)	--	--
DLCOPP	116.6 (± 20.3)	103.7 (± 17.0)	0.003

Results are the number (%) or mean (± standard deviation).

^*Groups were compared using t-tests for continuous variables and Chi-square or Fisher Exact tests (indicated by ⁺) for categorical variables. Statistically significant p-values are bolded if <0.005, adjusting for multiple comparisons (10 tests) with a Bonferroni correction.

^{^}One control is missing spirometry data. Spirometry values are all pre-bronchodilator and include the Forced Vital Capacity percent predicted (FVC_PP), Forced Expiratory Volume in one second (FEV1_PP), and the FEV1/FVC ratio. Residual volume percent predicted (RV_pp) was available for 43 deployers and diffusion capacity for carbon monoxide percent predicted (DLCO_pp) was available for 42 deployers and 41 controls.

We found no statistically significant differences in spirometry between the two groups, and almost all were normal. Lung volume (n=43) measurements were available only for deployers. Among 42 deployers and 41 controls with DLCO measurements available, deployers had significantly higher percent predicted DLCO (p=0.003). Twenty-five of 45 deployers had abnormally elevated residual volumes indicating hyperinflation; only one had an abnormally decreased DLCO. Twenty-five had deployment-related asthma (DRA); 24 had surgical lung biopsy findings of deployment-related distal lung disease (DDLD). Four deployers had both DRA and DDLD.

We found few differences in either the presence or extent of HRCT imaging abnormalities in deployers with biopsy-proven DDLD and/or DRA compared to controls (Table 2). The two most common chest CT imaging findings among deployers were mild-to-moderate expiratory air trapping and bronchial wall thickening (Figure 5). Expiratory air trapping was significantly more frequent in deployers compared to controls (47% vs 26%, p=0.04). Though more commonly noted in deployers, the frequency of bronchial wall thickening was not significantly different between groups (29% vs 15%, p=0.12). Patchy ground glass opacities (present in 1 deployer and 6 controls), bronchiectasis (1 deployer, 2 controls), reticulation (1 control), centrilobular or paraseptal emphysema (2 deployers, 3 controls), masses/nodules (1 deployer), and lymphadenopathy (1 deployer) were rare, and there were no significant differences between groups. Pleural abnormalities and honeycombing were not found in either group.

Table 2.

Comparison of HRCT imaging findings between controls and deployers with deployment-related asthma (DRA) and/or biopsy-proven deployment distal lung disease (DDLD)

	DDLD and/or DRA n=45	Controls n=46	p-value*
Bronchial Wall Thickening	13 (29%)	7 (15%)	0.12
Mosaic Attenuation (inspiratory)	0	2 (4%)	0.49+
Expiratory Air Trapping	21 (47%)	12 (26%)	0.04
Centrilobular Nodularity	1 (2%)	5 (11%)	0.20+
Centrilobular Emphysema	2 (4%)	1 (2%)	0.62+

^*Groups were compared using Chi-Square or Fisher Exact tests (indicated by ⁺). Statistically significant p-values are bolded if <0.05.

Fig 5 (a,b,c):

(a) Bronchial wall thickening in a 30-year-old never smoking deployer, (b, c) normal inspiratory CT (b) with moderate air trapping on expiratory CT (c) in a 36-year-old never smoking deployer

Adjusting for differences in age and smoking pack-years, odds of bronchial wall thickening (absent vs. present) were 2.18 (95% CI: 0.43, 11.22) times higher among deployers compared to controls (p=0.35). Neither age (p=0.48) nor smoking pack-years (p=0.07) were significantly associated with findings of bronchial wall thickening. Adjusting for differences in age and smoking pack-years, odds of air trapping were 1.09 (95% CI: 0.26, 4.55) times higher among controls compared to deployers (p=0.90). Age (p=0.03) was significantly associated with findings of expiratory air trapping, but smoking pack-years (p=0.09) was not. Full model estimates are included in S1 Table.

Among the deployers who underwent surgical lung biopsy, bronchiolitis and emphysema were found in 22/24 and 21/24 respectively (Table 3). Granulomatous pneumonitis was less common, affecting 13/24. Eleven of 24 deployers had all three findings on biopsy, and eight of 24 had both bronchiolitis and emphysema. Table 3 shows frequencies of HRCT findings relative to the spectrum of biopsy findings and among those with DRA. Imaging findings of expiratory air trapping were common in those with biopsy-proven bronchiolitis (10/22) and in those with deployment-related asthma (14/22). However, air trapping was not significantly more common in those with bronchiolitis compared to those without (p=0.49) or in those with DRA compared to those without (p=0.16). Bronchial wall thickening was noted in 9 of 25 with DRA but was not significantly more likely compared to those without DRA (4/20, p=0.25). None of the 24 deployers with DDLD on biopsy had findings of inspiratory mosaic attenuation or centrilobular nodularity on HRCT. Only two of 21 (9.5%) with hyperinflation/emphysema on biopsy had emphysema on HRCT.

Table 3.

Frequency of HRCT features in 24 deployers with DDLD and in 25 with deployment-related asthma (DRA)

HRCT features	Biopsy Findings of DDLD						DRA
	Granulomatous pneumonitis		Bronchiolitis		Emphysema
	No n=11	Yes n=13	No n=2	Yes n=22	No n=3	Yes n=21	No n=20	Yes n=25
Bronchial Wall Thickening	4 (36.4%)	1 (7.7%)	0	5 (22.7%)	1 (33.3%)	4 (19.1%)	4 (20.0%)	9 (36.0%)
Mosaic Attenuation (inspiratory)	0	0	0	0	0	0	0	0
Expiratory Air Trapping	6 (54.6%)	4 (30.8%)	0	10 (45.5%)	2 (66.7%)	8 (38.1%)	7 (35.0%)	14 (56.0%)
Centrilobular Nodularity	0	0	0	0	0	0	0	1 (4.0%)
Centrilobular Emphysema	1 (9.1%)	1 (7.7%)	0	2 (9.1%)	0	2 (9.5%)	1 (5.0%)	1 (4.0%)

Note: For the four deployers with both DRA and DDLD, findings are repeated between columns.

Figure 1 shows biopsy findings of emphysema and hyperinflation from two deployers, one with a paired HRCT image showing no emphysema or expiratory air trapping and the other with subtle emphysema on imaging. Constrictive/obliterative bronchiolitis in a deployer with persistent respiratory symptoms is shown in Figure 2, along with the subject’s normal HRCT image. Figure 3 shows extensive granulomatous pneumonitis in a deployer’s lung biopsy along with the paired HRCT image showing no distinctive imaging abnormalities. While several deployers had findings of pleuritis on biopsy (Figure 4), none had pleural abnormalities noted on HRCT.

Fig 1: Emphysema.

A) 24-year-old never smoking deployer with ten-month deployment history and findings of emphysema on biopsy, with normal chest HRCT. Arrows on histologic section point to broken alveolar tips at the periphery found in emphysema.

B) 50-year-old never smoking deployer with 7.3 year deployment history and findings of emphysema on biopsy, with subtle emphysema also on HRCT. Insert shows disruption of alveoli and typical club-shaped tips (arrows) of emphysema/hyperinflation.

Fig 2: Bronchiolitis.

48-year-old never smoking deployer with 22-month deployment history and findings of bronchiolitis on surgical lung biopsy, with normal HRCT. Histologic section shows constrictive concentric fibrosis interposed between the epithelium and smooth muscle bundles (double arrow) compressing the bronchiolar lumen. A few lymphoid aggregates (circles) are also evident within the bronchiolar wall.

Fig 3: Granulomatous pneumonitis.

43-year-old never smoking deployer with 23-month deployment exposure history and findings of granulomatous pneumonitis on surgical lung biopsy, with normal findings on HRCT. Histologic section shows a poorly formed granuloma (circle) with loose clusters of multinucleated giant cells (arrow) and epithelioid histiocytes.

Fig 4: Pleuritis.

57-year-old former smoking deployer (5 pack-years total) with 24-month deployment history, and findings of pleuritis on surgical lung biopsy with no pleural abnormalities on HRCT. Histologic section shows lymphocytic inflammation of the pleura (arrows). The pleural surface is indicated by the thick arrow.

As shown in Table 4, the majority (1,346 of 1,547, 87%) of imaging variables scored had complete agreement among all readers (a total of three for each case). Only 201 (13%) scoring disparities required use of the mode or grouping of similar severity scores for variable resolution. For all findings, at least two raters agreed on presence or absence. Based on Gwet’s AC1, agreement was extremely high (AC1≥0.80) for all findings except for air trapping where agreement was moderate (0.40) and for bronchial wall thickening where agreement was substantial (0.69). Kappa measures of agreement were lower than Gwet’s AC1. As noted above, Kappa statistics can be low despite high percent agreement among raters when prevalence is low, as is the case for most of the HRCT findings in this study.

Table 4.

Interrater Agreement between study radiologists (n=91 images)

	Finding Present	All Three Agreed	Gwet’s AC1	SE	p-value	Fleiss’ Kappa	SE
Bronchial wall thickening	72	59 (65%)	0.68	0.07	<0.0001	0.51	0.08
Mosaic attenuation (inspiratory)	7	86 (95%)	0.96	0.03	<0.0001	0.27	0.11
Expiratory air trapping	94	42 (46%)	0.40	0.12	0.001	0.27	0.07
Centrilobular nodularity	34	74 (81%)	0.84	0.06	<0.0001	0.43	0.12
Reticulation	5	89 (98%)	0.98	0.02	<0.0001	0.59	0.30
Traction bronchiectasis	3	91 (100%)	1	0	--	1	0
Honeycombing	0	91 (100%)	1	0	--	1	0
Ground glass	26	78 (86%)	0.88	0.07	<0.0001	0.45	0.12
Centrilobular Emphysema	13	83 (91%)	0.94	0.03	<0.0001	0.35	0.16
Paraseptal Emphysema	7	86 (95%)	0.96	0.03	<0.0001	0.27	0.11
Bronchiectasis	12	82 (90%)	0.93	0.05	<0.0001	0.22	0.20
Masses/nodules	21	73 (80%)	0.85	0.15	<0.0001	0.07	0.13
Lymphadenopathy	2	90 (99%)	0.99	0.01	<0.0001	0.50	0.004
Pleural disease	2	89 (98%)	0.99	0.01	<0.0001	−0.01	0.005

SE = Standard Error

Standard errors for both sampling among raters and items are presented for Gwet’s AC1. P-values were not available for Fleiss’ Kappa because of missing data between the two study radiologists that changed halfway through the study.

Discussion

Access to a cohort of symptomatic military deployers with biopsy-confirmed distal lung disease and/or clinically confirmed asthma provided a unique opportunity to evaluate the utility of visual assessment of chest HRCT as a noninvasive diagnostic tool for deployment-related lung disease.

We found that expiratory air trapping, often associated with obstructive small airways disease, was the only HRCT imaging finding that was significantly more common in deployers with persistent respiratory symptoms and clinically confirmed lung disease compared to controls. However, a number of investigators have shown that air trapping is present to some extent in the majority of healthy subjects and is more frequently seen in smokers on visual HRCT assessment.^31,32 Additionally, inter-observer agreement for this finding is often low, suggesting that expiratory air trapping by itself may have limited diagnostic utility for deployment-related lung disease.³³ Even with optimal image acquisition to enhance detection of airway abnormalities, centrilobular nodules and mosaic attenuation were usually absent in this population. If present, imaging abnormalities were subtle, nonspecific, and not readily distinguishable from controls. For example, thickening of walls of segmental and subsegmental airways, commonly associated with asthma, did not distinguish deployers with asthma from controls.³⁴ Distinct histologic findings of emphysema, bronchiolitis, and granulomatous pneumonitis were not identified by HRCT.

Use of a standardized scoring system by study radiologists helped assure comparable assessment of relevant if often subtle findings of airways abnormalities in all lung zones. The high rates of agreement among study thoracic radiologists, who found few imaging abnormalities, provides further confirmation that visual assessment of HRCT may have little utility in the diagnosis of bronchiolitis in symptomatic military deployers. These findings are similar to a study by King et al, who found that 6 of 38 deployed subjects with biopsy-proven bronchiolitis had air trapping on CT, and none were reported to have centrilobular nodules or mosaic attenuation.² The paucity of centrilobular nodularity on CT may reflect less severe peribronchiolar inflammation, though peribronchiolar metaplasia and smooth muscle hypertrophy are common findings in previously deployed personnel with respiratory symptoms.²⁰ Similarly, the absence of expiratory air trapping in the majority of subjects with bronchiolitis may be because the degree of bronchiolar abnormality was insufficient to cause complete bronchiolar obstruction. Notably, the deployer subjects had normal or near-normal spirometry. In a recent study of 50 soldiers with exertional dyspnea and biopsy confirmed small airways disease, over half (54%) had normal HRCT findings, with mild/moderate air trapping observed in 19 (36%).³⁷ Although CT findings of mosaic attenuation or air trapping may be helpful in suggesting the diagnosis of moderate or severe constrictive bronchiolitis, studies of post-transplant bronchiolitis have shown that air trapping is often absent in those with relatively mild obstruction.^35,36 If found to be accurate and generalizable in larger studies, our findings may be clinically helpful in discouraging the exclusion of deployment-related lung disease based on normal or minimally abnormal HRCT.

Our study has a number of strengths. Chest HRCT images were available from both symptomatic military deployers and controls matched as closely as possible for demographic and smoking variables. Study subjects were clinically well-characterized, providing an opportunity to test the sensitivity of HRCT in deployers with diagnoses of asthma, bronchiolitis or both. Moreover, half of participating deployers had histologically confirmed diagnoses of distal lung disease, the most sensitive diagnostic test available in early or indolent stages. Because we were able to correlate imaging findings with histologic abnormalities, the problem with misclassification bias present in many previous imaging studies of constrictive/obliterative bronchiolitis was avoided. Additionally, we pilot-tested and implemented an electronic chest HRCT scoring system for use by study radiologists and data analysts. We employed the same image acquisition protocol for all study participants, incorporating dynamic inspiratory and expiratory acquisition. All of the study radiologists were blinded to clinical status and to each other’s interpretations.

Our findings are subject to several limitations. First, the number of study subjects was not large and may not be representative of the larger population of previously deployed veterans with post-deployment respiratory symptoms. Second, significant demographic differences between deployers and controls (with controls being older and having more smoking pack-years) may have limited our ability to detect imaging differences between groups. For example, bronchial wall thickness varies with age and smoking status. Additionally, we would expect previously deployed military personnel to have higher levels of aerobic fitness (compared to the control group) that could affect their baseline lung function. Further, while smoking histories were available for controls, we did not have information on exposure histories or co-morbidities (except asthma) that may have influenced HRCT findings. Third, radiologists may not have been fully blinded to study subject status due to subtle differences in technique between deployer and control images. Finally, all of the study radiologists came from the same institution, where approaches to image interpretation and scoring may overlap substantially compared to radiologists from other institutions.

Conclusions

Given the limitations of HRCT visual assessment for detection of deployment-related distal lung disease observed in this cohort, along with cost and radiation dose, alternative approaches to diagnosis may be needed. Techniques such as quantitative image analysis of airway wall thickening and emphysema³⁸ and functional lung imaging for ventilation inhomogeneity³⁹ may be more sensitive than visual assessment of HRCT in detecting distal airways abnormalities. Other modalities such as lung clearance index testing, separately or in combination with quantitative image analysis, may enhance diagnostic sensitivity⁴⁰ and await further investigation.

Supplementary Material

Supplemental Data File (.doc, .tif, pdf, etc.)_1

S1 Table. Odds of imaging findings among deployers and controls adjusted for age and smoking pack-years.

Supplemental Data File (.doc, .tif, pdf, etc.)_2

Supplement 2. HRCT Image Scoring Form