ObjectivesTo quantify airway and artery (AA)-dimensions in cystic fibrosis (CF) and control patients for objective CT diagnosis of bronchiectasis and airway wall thickness (AWT).MethodsSpirometer-guided inspiratory and expiratory CTs of 11 CF and 12 control patients were collected retrospectively. Airway pathways were annotated semi-automatically to reconstruct three-dimensional bronchial trees. All visible AA-pairs were measured perpendicular to the airway axis. Inner, outer and AWT (outer−inner) diameter were divided by the adjacent artery diameter to compute AinA-, AoutA- and AWTA-ratios. AA-ratios were predicted using mixed-effects models including disease status, lung volume, gender, height and age as covariates.ResultsDemographics did not differ significantly between cohorts. Mean AA-pairs CF: 299 inspiratory; 82 expiratory. Controls: 131 inspiratory; 58 expiratory. All ratios were significantly larger in inspiratory compared to expiratory CTs for both groups (p<0.001). AoutA- and AWTA-ratios were larger in CF than in controls, independent of lung volume (p<0.01). Difference of AoutA- and AWTA-ratios between patients with CF and controls increased significantly for every following airway generation (p<0.001).ConclusionDiagnosis of bronchiectasis is highly dependent on lung volume and more reliably diagnosed using outer airway diameter. Difference in bronchiectasis and AWT severity between the two cohorts increased with each airway generation.Key points • More peripheral airways are visible in CF patients compared to controls. • Structural lung changes in CF patients are greater with each airway generation. • Number of airways visualized on CT could quantify CF lung disease. • For objective airway disease quantification on CT, lung volume standardization is required. Electronic supplementary materialThe online version of this article (doi:10.1007/s00330-017-4819-7) contains supplementary material, which is available to authorized users.

Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently shown that in overfitted mixture models, the overfitted latent classes will asymptotically become empty under specific conditions for the prior of the class proportions. This result may be used to construct a criterion for finding the true number of latent classes, based on the removal of latent classes that have negligible proportions. Unlike some alternative criteria, this criterion can easily be implemented in complex statistical models such as latent class mixed-effects models and multivariate mixture models using standard Bayesian software. We performed an extensive simulation study to develop practical guidelines to determine the appropriate number of latent classes based on the posterior distribution of the class proportions, and to compare this criterion with alternative criteria. The performance of the proposed criterion is illustrated using a data set of repeatedly measured hemoglobin values of blood donors.