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The dawn of precision medicine and CFTR modulators require more detailed assessment of lung structure in cystic fibrosis (CF) clinical studies. High-resolution chest computed tomography (CT) scoring has yielded sensitive markers for the study of CF disease progression and therapeutic effectiveness. Similarly, Magnetic Resonance Imaging (MRI) is in development to generate structural as well as functional markers.
The aim of this review is to characterize the role of CT and MRI markers in clinical studies, and to discuss study design, data processing and statistical challenges unique to these endpoints in CF studies. Suggestions to overcome these challenges in CF studies are included.
To maximize the potential of CT and MRI markers in clinical studies and advance treatment of CF disease progression, efforts should be made to develop data repositories, promote standardization and conduct reproducible research.
High-resolution chest computed tomography (CT) has produced promising outcomes for the clinical study of cystic fibrosis (CF) lung disease progression(1, 2). Markers from CT imaging can convey severity related to mild and regional CF lung disease by quantifying degrees of bronchiectasis, air trapping, and other attributes related to structural lung damage. It has been shown that CT markers have higher sensitivity to detect pulmonary disease progression than FEV1%, an established outcome in CF(3–7). More recently, magnetic resonance imaging (MRI) techniques have emerged to provide radiation-free markers that quantify structural and dynamic aspects of the CF lung(8). Given the challenges related to pulmonary function testing in younger individuals with CF and the need for assessments of early-stage CF lung disease, clinical studies have incorporated CT markers as outcomes for structural lung disease(9). However, there remain obstacles related to study design, data acquisition/processing, and statistical analysis that have not been comprehensively addressed, hindering more complete adaptation of established CT markers as endpoints(10). The aim of this review is to provide information on the utility of imaging markers as endpoints for CF studies and to describe the accompanying statistical considerations. CT serves as the exemplar for the review, given its status as the gold standard for lung structure assessment; however, considerations shared by MRI are also described and accompanied by recommendations.
The clinical study of imaging markers has evolved from qualitative evaluation to quantitative lung image analysis. Although seminal work involved the study of markings from chest radiographs, this modality has largely been excluded from clinical intervention studies due its poor sensitivity for monitoring CF disease progression (11) the advent of CT scoring systems. Over the last few decades it has been shown that bronchiectasis, airway wall thickening, mucous impaction, and trapped air are the most important markers to quantify on chest CTs. It becomes clearer that the term “trapped air” is probably a misnomer, as hypodense regions on expiratory CT can result both from hypoperfusion and trapped air. For the purpose of this review, we will continue to use the term trapped air, acknowledging it represents a mix of hypoperfusion and trapped air(12).
Semi-quantitative image analysis of CT scans to assess these attributes has yielded a variety of scoring systems that have been used for clinical studies (Table 1). The Brody I system was developed in 1999 using CTs of 8 patients aged 5–16 years. At that time, CF lung disease progressed more rapidly, compared to the present day(13). The Brody II scoring system (14, 15) followed and was frequently used until a decade ago. De Jong and colleagues compared various scoring systems and found that they were reproducible and correlated with pulmonary function data(16); however, these scoring systems were not well-standardized.
To improve standardization and training, the CF-CT scoring system, based on the Brody II system, was developed in 2011. The CF-CT scoring system consists of a large training module and 7 training sets that were scored by Brody and de Jong (the most experienced observers at that time) to define the ‘gold standard’ ratings. To date, over 20 observers have been trained in the Erasmus MC LungAnalysis Core Laboratory using the CF-CT method. It has been used in multiple studies to validate chest CT as an outcome (6, 17–20). An advantage of scoring systems like CF-CT is that the lung volume level during CT acquisition is not very critical for the magnitude of the scores (21) (22).
The CF-CT scoring system still has a number of disadvantages. Firstly, it is insensitive for quantifying early changes (23), only detecting relatively large structural changes over time. Secondly, the clinical value of the numbers generated is difficult to understand. Thirdly, the method is time consuming and observer dependent. Hence, further development of more sensitive methods was required.
For the development of a more sensitive and quantitative method, a morphometric approach was created using a grid projected over the CT image. This approach was used first in a group of 411 patients with end stage lung disease (24, 25) and later to compute volume fractions of trapped air on expiratory scans(26). Next, this method was further developed into the Perth-Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF) scoring system to quantify early structural changes(18). This system allows expression of key structural changes, i.e. airway abnormalities and regions of low density, as a fraction of total lung volume. This system can also be applied in more advanced disease(24). A disadvantage of PRAGMA-CF is that it requires two weeks of training and takes around 30 minutes per CT to execute for an experienced observer. Fortunately, it is likely that the system can be automated using a machine learning approach. More recently, the Airway-Artery (AA) method was developed for the sensitive and automated analysis of all visible airway artery pairs. It is likely that this system eventually will take over the scoring of airway abnormalities (Kuo and colleagues, 2016, work in press and other under review).
Image analysis systems are at an early stage of development for MRI-based quantification (27, 28). Failo and colleagues, among others, have reported that CT and MRI modalities produce similar Brody scores(29, 30). A small study of MRI perfusion markers obtained on non-CF adults suggested that scores might be highly dependent upon observer(31). The sensitivity, extent of reproducibility, and repeatability of MRI-based scoring systems needs further study.
Quantitative image analysis paved the way for reproducible, reliable CT scoring systems that can be used to produce outcomes for clinical studies. Currently, pulmonary exacerbation, health-related quality of life, pulmonary function and survival are the only recognized clinical endpoints for CF studies(10, 32). As surrogate endpoints, imaging markers do not directly measure how an individual with CF “functions, feels or survives”(33). CT markers, meant to assess structure, are often considered as intermediate endpoints in CF studies due to their ability to predict established clinical endpoints(2, 19, 24). MRI is a more promising modality than CT to assess functional aspects of the lung, such as lung perfusion, pulmonary hemodynamics, central airway dynamics and ventilation of the lung(31, 34).
Given the pathophysiology of CF, it is likely that the imaging marker is measured (perhaps repeatedly) with a particular therapy being applied at some point in the disease process (Figure 1). Other surrogates, such as FEV1%, are repeatedly collected throughout this process and may impact clinical endpoints independently of the imaging marker. A CT marker, for example, is a useful surrogate, provided it is consistently i) predictive of future events; ii) reflective of a therapeutic response(35). Loeve and colleagues summarized over 20 studies validating CT markers as surrogate endpoints for presence and severity of CF lung disease, therapeutic responsiveness, reproducibility, and associations with respiratory exacerbations, quality of life, survival and other outcomes(26). As indicated, future validation studies should focus on criterion (ii), in order to further demonstrate surrogacy.
CT scans yield a variety of structural markers, and the choice of which is used for a particular CF study will depend upon several factors, such as the age and severity of the population being studied, therapy being evaluated, and duration of the study. It is important to determine the extent to which a prospective CT marker is predictive of the clinical endpoint and whether the therapeutic response of the CT marker (as a surrogate) is predictive of the therapeutic response detected by the clinical endpoint. CT markers that have scoring systems independently validated prior to the study, have been utilized in previous CF clinical studies and are most closely aligned with the therapeutic aim and causal pathway should be considered as surrogates. Similar factors should be considered when selecting MRI markers as monitoring tools for clinical studies. Timing of study visits could also be considered, as MRI may be utilized to assess short-term changes in therapeutic studies(31, 36).
An issue that has received little attention until recently is the impact of standardizing image acquisition techniques. Controlling lung volume is an important issue in the acquisition phase. In children of 6 years of age and above, lung volume can best be controlled for using a spirometer(37). Participants below the age of 6 years may have difficulty following spirometer-based protocols; in these instances, general anesthesia and a pressure-controlled protocol can be used for lung volume control(21). Children aged 3–6 years can be trained to execute a breath hold after taking a deep breath or at the FRC level. Alternatively, CT can be acquired for very young or non-cooperative children while the child is free breathing. For these children, scans acquired at a volume level near functional residual volume are less sensitive, compared to inspiratory scans for the detection of airway disease. Equally important factors to consider include radiation dose, pitch, reconstruction kernels, and slice thickness; all of which are known to influence image quality(17) and can be accounted for in prospective studies.
The selection of the CT protocol is closely linked to the image analysis methods used. When a (semi) automated analysis method is selected, tighter control of the CT protocol is required, but scoring methods are known to be less sensitive to choice of CT protocol and the aforementioned issues with lung volume control(22). To track disease over time, ideally the same volume and CT protocol should be used when follow up CTs are compared to baseline CTs. Recent work highlights the importance of standardization in multicenter studies using CT(17). In addition to previously mentioned longitudinal validation studies, assessment of standardization techniques are also needed for MRI. However, this issue is considered an important technical challenge for MRI.
Age can be a substantial confounder as the chest CT resolution is an important determinant for the smallest structures that can still be observed. This is especially important in the first two years of life. Minimizing confounding through study design is ideal. It may be possible to use methods based on restriction or matching. Examples include subgroup or covariate adjustment to determine the extent of technology, processing or other effects on analysis results. These stratification and multivariable modeling approaches rely on measured confounders (i.e. the variables are recorded in the database).
Despite advancements toward automation, CT scoring systems still require observer evaluation to quantify degree of structural lung disease, and there is no gold standard metric to which observers can be compared. As such, observer agreement and reliability are indistinguishable and often used synonymously in the CF imaging literature. Observer scoring introduces variation known as measurement error (38). It is worth noting that, in contrast, automated scoring might introduce systematic error (e.g. over- or underestimation of airway wall thickness). The following recommendations on observer reliability apply generally to CT and MRI markers.
At minimum, it is recommended that any study with scored CT markers include reliability statistics within observer. Intra-rater reliability refers to the extent to which a single observer can replicate his previous scores on a series of scans. In terms of experimental design, the scans to be repeatedly scored should be selected using random sampling stratified by age and disease severity. This approach should be employed in CF studies, because of the heterogeneous nature of disease progression. For example a study may yield high reliability in the subcohort with severe disease and low reliability in the subcohort with mild disease.
The proportion of between-subject variation relative to the total variation is commonly used to estimate reliability. Shoukri and colleagues provide sample size calculations based on precision with which reliability can be estimated for test-retest data from one or more observers(39). This approach uses the confidence interval (CI) width as a measure of precision, level of confidence (i.e. alpha value), and specification of a “planning value” for the reliability estimate. For example, if high intra-rater reliability is anticipated (proportion: 0.9) with sufficient precision (95% CI width: 0.2), the minimum number of scans to be rescored is 15. Sample size requirements will increase for the following inputs: lower values of planned reliability, higher precision and a higher confidence level. It is recommended that test-retest sample size, like other sample sizes described later, be calculated in the study design phase, as it will heavily depend upon the formula inputs, funding and other resource constraints.
Agreement between the primary observer (an experienced or certified observer whose scores are considered the benchmark) and other observer(s) is needed to check consistency. CT studies typically involve a primary observer and at least one other independent observer who is trained in the scoring system as part of the study. Depending on the extent to which a scoring system has been validated for the target population, it is important to have well-calibrated scores. This process is often qualitative and consists of a subset of scans, chosen by the primary observer, being iteratively scored by the novice observer who discusses results with the primary observer. It is recommended that the calibration subset be chosen via stratified randomization. The scans should be selected through random sampling stratified according to age and lung disease severity, to ensure calibration is broadly achieved. Sample size requirements for estimating inter-rater reliability can be formed as previously described. For example, if we consider two independent observers to have excellent agreement on their scores (proportion: 0.8) with sufficient precision (95% CI width: 0.2), the minimum number of scans to be scored by each observer is 52. If acquiring high numbers of subjects for imaging is more costly than utilizing multiple observers, then the sample size can be recalculated to include a higher number of observers. Assuming the same agreement and precision as before but utilizing three observers, for example, requires a minimum of 36 scans to be scored.
There are numerous inter-rater reliability indices available for CT studies (Table 2). In the majority of CF studies, the index of choice is the intra-class correlation coefficient (ICC)(40). There are many versions of ICC, but the most commonly utilized version in the CF literature is the original based on the one-way random effects ANOVA model, which assumes that raters are interchangeable. In studies in which there is an experienced rater, the one-way random effects ANOVA can be modified to assess consistency across the different raters. Drawbacks to use of ICC and its categorical analogue, the kappa statistic (41), have been well described in the statistics literature, with a recent illustration involving cardiovascular imaging markers(42). A high estimate of ICC for an overall study sample does not always indicate strong agreement. For example, a CF study with participants who vary in age will likely yield CT markers with broad ranges (i.e., high between subject variability). Regardless of observer agreement, the ICC ratio will unfairly leverage the high between-subject variability relative to within-subject variability to produce a value close to 1. Analogously, a study with a narrow subject age range may yield small between subject variability for each CT marker with ICC estimates that are incorrectly low. For these reasons, it is important to stratify ICC estimates by age and disease severity, which are known sources of heterogeneity in CF clinical studies. Poor ICC estimates within strata imply that additional calibration is necessary.
An alternative reliability statistic that relaxes the ANOVA assumption in the ICC is the Concordance Correlation Coefficient (CCC) (43); however, this statistic does not account for chance agreement and has not been widely used to assess reliability of imaging markers. Threshold-based approaches, such as Bland-Altman analysis(44) and coverage probability (CP), allow more targeted identification of discrepancies. The Bland-Altman approach consists of plotting the mean of differences between observer measurements of a given CT marker against the average of measurement pairs from the observers; limits of agreement on the plot can be used to identify systematic differences in observers across the range of measurement values, or instances of increased variation (i.e. lower precision) between observers. These limits are determined by mean difference, standard deviation of the differences and sample size. CP has the most advantages of the methods described (Table 2) for all stages of reliability assessment, but requires a priori identification of an acceptable threshold for the paired difference in observer scores.
Given the breadth of available reliability indices for CT markers, it is recommended that researchers report estimates for their selected reliability index and descriptive statistics for each type of CT marker, both overall and stratified by age and lung disease severity. The additional estimates can be used to calculate other reliability indices, such as CP, enabling ‘apples-to-apples’ comparisons of reliability estimates across CF studies and populations.
Baseline CT collection in clinical trials is necessary for assessing randomization, safety and quality of procedure/protocol adherence. Even in modified protocols described previously, young children (aged below 5 years) will not have the same participant performance in CT studies at baseline as they do in follow-up. It is recommended that the mean and variability of baseline CT data be examined prior to inclusion in efficacy analyses. The coefficient of variation, which is the SD divided by the mean in the sample, can be used to examine the quality of baseline data. A large value for this ratio would imply that the baseline data may not be reliable to assess efficacy; however, these data could still be used for phenotyping the patient and checking randomization. These issues do not exist for child participants who are 5 years of age or older; change scores or other longitudinal variables have been validated in previous CF studies(3, 5, 19) and can be used in efficacy analyses.
As an individual grows, the sensitivity of a given CT marker will increase, due to airway development, improved ability to perform protocol steps (e.g. breath holds) and other factors. Growth introduces subtle changes related to the CT scanner performance that have not been thoroughly explored in CF studies. Discarding any baseline data is undesirable from a statistical standpoint. Depending on the coefficient of variation, it may be appropriate to include data from very young cohorts by adjusting for growth using established metrics. This could possibly be achieved using metrics as covariates or by performing a calibration analysis. The effectiveness of these approaches to salvage baseline CT data is unknown but could be investigated with the advent of young CF cohorts with imaging data.
The collection of CT marker data on an individual subject will consist of overall and region-specific scores. We will need to assume that the outcome variable, given a treatment and possibly other exposure variables, forms a model that follows some type of statistical distribution. Most often, we assume a (multivariate) normal distribution. Distributions of CT markers and modeling assumptions should be assessed and reported in clinical studies. A normal distribution may be reasonable for CT markers observed at later stages of disease severity, but it is plausible in early-stage CF to encounter subjects whose CT markers are long-tailed (e.g. log-normal) or have zero values (e.g., bronchiectasis). Zeroes may be indicative of minimal or absent structural lung disease, an outcome that may be of interest itself. The data may appear “clumped” at zero, as there could be a proportion of subjects who at a young age have not experienced an insult in addition to their CFTR dysfunction resulting in bronchiectasis or other structural lung changes at a young age. Examples of such additional insults can be viral infection or acquisition of Pseudomonas aeruginosa. The remaining subjects who have developed structural lung disease will have continuous values for CT markers. The resulting data forms a skewed distribution that may not be well approximated with a symmetric, bell-shaped curve like the normal distribution. Data of this nature are sometimes referred to as zero-inflated or semicontinuous data(45).
Examples of non-normally distributed data have been recently encountered in the Australian Respiratory Early Surveillance Team for CF (AREST CF) cohort (Figure 2). PRAGMA-CF markers for disease and bronchiectasis, obtained with permission from the previously published cohort data(9), have substantial lack of fit under the normal assumption. The mean and SD are both overestimated for % disease and % bronchiectasis. The fit to the % disease data is improved by accounting for the skewness using a lognormal distribution. Special care is required to fit the % bronchiectasis data, as roughly 42% of the data are zeroes. Min and Agresti reviewed and discussed practical strategies to combat zero-inflated and semi-continuous data(45), which can be extended for longitudinal studies through use of random effects(46). Assuming a normal distribution in either scenario could yield biased estimates and misleading results about the extent of structural lung disease in the cohort being studied, and about the effects of treatment or associations with other exposure variables. Approaches to model CT markers and their relationships with covariates using alternative distributions should be considered for CT as well as for MRI marker data analysis.
In prospective CT studies, participation will depend upon the eagerness of the individual subject. If the study has an intervention arm, participation will generally be high for CF subjects, regardless of whether CT scans are part of the protocol. Subject retention may be complicated in longitudinal settings in which it is expected that a subset of participants could drop out, miss scans, or receive scans at times that are not commensurate with the protocol (creating mistimed measurements). The nature of missing data in CT studies can also include technical failures, protocol violations (e.g. inappropriate data storage or slice thickness), and subject noncompliance. At the very least, missing data can reduce efficiency, thereby limiting statistical power; at worst, missing data can seriously bias study findings. In CT studies, this could imply incorrect conclusions about structural lung disease progression over time, or limited ability to detect efficacy in CF therapeutic studies. Practical methods to limit missing data and address the potential bias via statistical analyses, accounting for the missing data mechanism, are available(47).
In retrospective CT analyses, caution should be applied when selecting individual scans for inclusion. It has been shown in US CF registry analyses that sicker patients tend to have more clinical encounters. Because these patients are sicker, they tend to have worse outcomes(48). Such issues are avoided when only routine clinical scans are included in the analysis. It is recommended to randomly select scans for inclusion, if the study is retrospective. In prospective studies, such as clinical trials, the impact of including clinically-indicated scans could be assessed through sensitivity analyses. Ignoring this source of sampling bias could produce misleading results about associations between treatment and progression of structural lung disease as measured by CT.
Several CT markers have been developed over the years and have varying levels of sensitivity (Table 1). CT marker variability depends upon the type of cohort being studied, the scan protocol and scoring algorithm. Minimizing these sources of variability, in turn, maximizes the precision with which treatment effects or associations can be examined using a particular CT marker. Bronchiectasis and trapped air can be established with great precision and are well validated as clinically relevant endpoints. Scoring of airway thickness is more system dependent. The recently objective AA method allows assessment of both bronchiectasis and airways wall thickness with great precision; however, this method is time-consuming and not yet automated. The less intensive PRAGMA-CF scoring system has been shown to correlate well with the AA method and thus is currently the best available method as shown in published work (18) and studies now under review
CT marker effect sizes have not been thoroughly described in the literature, although estimates may be gleaned from completed studies (Table 1). None of the current CT markers have a designated minimally important clinical difference, a threshold used to indicate the smallest change in outcome that a patient would still identify as clinically important. Percent reduction for a given CT marker has been proposed as a biologically plausible outcome for CF clinical studies(18). For example, a 30% reduction in trapped air would be considered clinically relevant. It is worth noting that whatever particular feature the CT marker is intended to measure also determines clinical meaning of the % reduction. For instance, extent of bronchiectasis is a monotonically increasing attribute of structural lung disease, whereas trapped air may be reversed, to some extent, over time.
Both the choice of CT marker and threshold indicating clinically meaningful % reduction will impact sample size required for an interventional study (Figures 2c–2d). Effect sizes were formulated based on mean PRAGMA % disease and % bronchiectasis in children aged 0–5 in AREST CF cohort (9) and were calculated as a % difference between means of two hypothetical treatment arms, assuming a traditional two group clinical trial design (i.e. mean % disease in children aged 0–5 is 1.90, 30% reduction would imply that in the interventional trial arm mean % disease would be reduced to 1.33, 50% would reduce it to 0.95, etc.). Higher magnitudes of the relative difference in % disease (Figure 2c) and % bronchiectasis (Figure 2d) allow for lower sample size requirements per group. Given the relatively low numbers of CF participants, it is likely that only large magnitudes of relative difference will be detectable in interventional studies using scoring systems such as PRAGMA-CF. More sensitive outcome measures, such as the AA-ratio, have the potential to improve precision, thereby enabling detection of relative difference with lower sample sizes.
With the number of imaging studies in CF and the advent of (semi) quantitative methods, there are several ways to improve the utility of CT and MRI markers. CT markers have been well validated in the literature, but their role in the randomized controlled trial setting still needs to be proven. MRI markers for studying CF lung disease progression are at an early stage; research to date indicates that this modality tends to overestimate the extent of early disease and underestimate advanced disease. Given the current findings on standardization for each modality, CT and MRI markers may be feasible for Phase III and single center Phase II studies, respectively. There is now a CTN-TDN task force, composing guidelines that will further harmonize study protocol recommendations, complimenting existing efforts in the European Union that have been published (17) and are under review for CT. Similar efforts have recently been initiated for MRI. Furthermore, minimal barriers exist to implementing these standards for routine clinical scans. Clinical standardization at CTN-TDN sites is expected to decrease bias inherent in the historical (less standardized) CT scanning and data collection methods, thereby enabling data pooling. Another strategy to minimize bias in MRI and CT studies is training and certification of observers. Additional standards for scoring systems should include randomized ordering, de-identification of scans, uniform lighting conditions, and well-defined analysis time. The advent of automated image analysis systems is also expected to decrease inherent bias in CT and MRI data processing.
Reproducibility is essential to elevate pre-processing and statistical analysis standards for imaging studies. Researchers should use online supplements to provide detailed data summaries, including formulas for pre-processing, aggregate calculations and statistical considerations, as in the recent study by Ramsey and colleagues (9). Accessing completed studies via data repositories could also streamline development of novel and robust imaging analysis methods. Such repositories could be leveraged to gain historical control data for examining novel therapies, similar to the use of CF patient registries to gain historical control data as comparisons of clinical trial findings. As reproducibility improves, annual reporting of summary statistics or trends in clinical data, such as lung function, BMI, and other markers reported by the US CF Foundation(49), could be expanded to include CT markers. With multiple modalities being used in parallel, efforts to use CT in conjunction with MRI and other markers can be considered to effectively monitor CF lung disease.
Although challenges with reliability, model assumptions and missing data are still at the forefront of imaging marker analysis, new challenges will emerge with more automated scoring systems, requiring development of spatial data models to understand detailed lung structure and consideration of multiple comparison adjustments as debated in the neuroimaging literature(50). Additional prospective longitudinal studies that include both imaging markers and functional outcomes will be helpful to examine associations between the two evolve over time, improving our understanding of both imaging markers and of the more traditionally used functional markers.
The authors are grateful to Tim Rosenow, BSc, Grad Cert PaedRespSci, PhD Candidate, and Mekibib Altaye, PhD, for their critical reading of the manuscript and valuable comments. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH/NHLBI.
Conflict of Interest
Author RS received support for this work from the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health (NIH) under award number K25 HL125954. Author HT acted in the last 4 years as consultant for the Sophia BV of the Erasmus MC Sophia Children’s Hospital on advisory boards for Gilead Sciences, Novartis Pharmaceuticals, Insmed, Vertex, and PTC. In addition the Sophia BV received speaker fees for presentations by HT from Gilead, Vertex, and Roche. He does not own any stock. The Sophia BV received unconditional research grants from Roche, Chiesi, Novartis and Gilead for research supervised by HT. He is the founder and director of Erasmus MC LungAnalysis core laboratory for image analysis under the co-supervision of the research bureau of the department of Radiology.
Rhonda Szczesniak, Division of Biostatistics & Epidemiology and Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center; Cincinnati, OH United States.
Lidija Turkovic, Telethon Kids Institute, West Perth, Australia.
Eleni-Rosalina Andrinopoulou, Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands.
Harm A. W. M. Tiddens, Department of Pediatric Pulmonology and Allergology, Department of Radiology Erasmus MC-Sophia Children’s Hospital, Rotterdam, The Netherlands.