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Objectives: Airway morphology shows characteristic changes in different pathologies. This study assesses the accuracy of a current automatic airway assessment technique by correlating CT images of porcine airways to histological slices of the same localization. Materials and Methods: Four isolated and ventilated porcine lungs were frozen in a liquid nitrogen bath and examined with a CT scanner (MDCT). This technique both preserved normal radiomorphological appearance and made it possible to slice the specimens for histological examination for subsequent correlation. The parameters wall thickness (WT), wall percentage (WP), and total diameter (TD) were assessed by computer-aided measurement of the MDCT images using an integral-based method (IBM) and by manually measuring the histological slices with an electronic caliper. Results: The radiological-pathological correlation could be performed for 16 localizations. Mean relative errors for WT, WP, and TD were 11%, 5.6%, and 8.5%, respectively. Correlation was very high with coefficients r of 0.951 for WT, 0.916 for WP, and 0.987 for TD. Conclusions: Our results are comparable to previously described errors in phantom correlations but are the first proof of ex vivo feasibility. Thus, by applying this freezing technique to MDCT data of diseased, explanted lungs and by combination with the IBM, further experiments can be performed to explore the effects of airway pathology on radiological morphology. [ABSTRACT FROM AUTHOR]

Quantitative assessment of airway-wall dimensions by computed tomography (CT) has proven to be a marker of airway-wall remodelling in chronic obstructive pulmonary disease (COPD) patients. The objective was to correlate the wall thickness of large and small airways with functional parameters of airflow obstruction in COPD patients on multi-detector (MD) CT images using a new quantification procedure from a three-dimensional (3D) approach of the bronchial tree. In 31 patients (smokers/COPD, non-smokers/controls), we quantitatively assessed contiguous MDCT cross-sections reconstructed orthogonally along the airway axis, taking the point-spread function into account to circumvent over-estimation. Wall thickness and wall percentage were measured and the per-patient mean/median correlated with FEV1 and FEV1%. A median of 619 orthogonal airway locations was assessed per patient. Mean wall percentage/mean wall thickness/median wall thickness in non-smokers (29.6%/0.69 mm/0.37 mm) was significantly different from the COPD group (38.9%/0.83 mm/0.54 mm). Correlation coefficients (r) between FEV1 or FEV1% predicted and intra-individual means of the wall percentage were -0.569 and -0.560, respectively, with p < 0.001. Depending on the parameter, they were increased for airways of 4 mm and smaller in total diameter, being -0.621 (FEV1) and -0.537 (FEV1%) with p < 0.002. The wall thickness was significantly higher in smokers than in non-smokers. In COPD patients, the wall thickness measured as a mean for a given patient correlated with the values of FEV1 and FEV1% predicted. Correlation with FEV1 was higher when only small airways were considered. [ABSTRACT FROM AUTHOR]

Objectives: Presentation and validation of software developed for automated and accurate application of a reference based algorithm (reference sphere method: RSM) inferring the effective imaging geometry from quantitative radiographic image analysis. Methods: The software uses modern pattern recognition and computer vision algorithms adapted for the particular application of automated detection of the reference sphere shadows (ellipses) with subpixel accuracy, it applies the RSM algorithm to the shadows detected, thereby providing three-dimensional Cartesian coordinates of the spheres. If the three sphere centres do not lie on one line, they uniquely determine the imaging geometry. Accuracy of the computed coordinates is investigated in a set of 28 charge coupled device (CCD)-based radiographs of two human mandible segments produced on an optical bench. Each specimen contained three reference spheres (two different radii r1 = 1.5 mm, r2 = 2.5 mm). True sphere coordinates were assessed with a manually operated calliper. Software accuracy was investigated for a weighted and unweighted algebraic ellipse-fitting algorithm. Results: The critical depth-(z-) coordinates revealed mean absolute errors ranging between 1.1 ± 0.7mm (unweighted version; r = 2.5mm) and 1.4 ± 1.4 mm (weighted version. r = 2.5 mm), corresponding to mean relative errors between 5% and 6%. Outliers resulted from complete circular dense structure superimposition and one obviously deformed reference sphere. Conclusions: The software provides information fundamentally important for the image formation and geometric image registration, which is a crucial step for three-dimensional reconstruction from ≥2 two-dimensional views. [ABSTRACT FROM AUTHOR]

Purpose To quantify tracheal collapsibility using low-dose four-dimensional (4D) CT and to compare visual and quantitative 4D CT-based assessments with assessments from paired inspiratory-expiratory CT, bronchoscopy, and spirometry. Materials and Methods The authors retrospectively analyzed 4D CT examinations (January 2016-December 2022) during shallow respiration in 52 patients (mean age, 66 years ± 12 [SD]; 27 female, 25 male), including 32 patients with chronic obstructive pulmonary disease (mean forced expiratory volume in 1 second percentage predicted [FEV 1 %], 50% ± 27), with suspected tracheal collapse. Paired CT data were available for 27 patients and bronchoscopy data for 46 patients. Images were reviewed by two radiologists in consensus, classifying patients into three groups: 50% or greater tracheal collapsibility, less than 50% collapsibility, or fixed stenosis. Changes in minimal tracheal lumen area, tracheal volume, and lung volume from inspiration to expiration were quantified using YACTA software. Tracheal collapsibility between groups was compared employing one-way analysis of variance (ANOVA). For related samples within one group, ANOVA with repeated measures was used. Spearman rank order correlation coefficient was calculated for collapsibility versus pulmonary function tests. Results At 4D CT, 25 of 52 (48%) patients had tracheal collapsibility of 50% or greater, 20 of 52 (38%) less than 50%, and seven of 52 (13%) had fixed stenosis. Visual assessment of 4D CT detected more patients with collapsibility of 50% or greater than paired CT, and concordance was 41% ( P < .001). 4D CT helped identify more patients with tracheal collapsibility of 50% or greater than did bronchoscopy, and concordance was 74% ( P = .39). Mean collapsibility of tracheal lumen area and volume at 4D CT were higher for 50% or greater visually assessed collapsibility (area: 53% ± 9 and lumen: 52% ± 10) compared with the less than 50% group (27% ± 9 and 26% ± 6, respectively) ( P < .001), whereas both tracheal area and volume were stable for the fixed stenosis group (area: 16% ± 12 and lumen: 21% ± 11). Collapsibility of tracheal lumen area and volume did not correlate with FEV 1 % ( r s = -0.002 to 0.01, P = .99-.96). Conclusion The study demonstrated that 4D CT is feasible and potentially more sensitive than paired CT for central airway collapse. Expectedly, FEV 1 % was not correlated with severity of tracheal collapsibility. Keywords: CT-Quantitative, Tracheobronchial Tree, Chronic Obstructive Pulmonary Disease, Imaging Postprocessing, Thorax Supplemental material is available for this article. © RSNA, 2024.

Objectives: A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema., Methods: A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ)., Results: The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT., Conclusion: Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features., Clinical Relevance Statement: MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered., Key Points: • In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT., (© 2024. The Author(s).)

Background: Effective detection of early lung disease in cystic fibrosis (CF) is critical to understanding early pathogenesis and evaluating early intervention strategies. We aimed to compare ability of several proposed sensitive functional tools to detect early CF lung disease as defined by CT structural disease in school aged children., Methods: 50 CF subjects (mean±SD 11.2 ± 3.5y, range 5-18y) with early lung disease (FEV 1 ≥70 % predicted: 95.7 ± 11.8 %) performed spirometry, Multiple breath washout (MBW, including trapped gas assessment), oscillometry, cardiopulmonary exercise testing (CPET) and simultaneous spirometer-directed low-dose CT imaging. CT data were analysed using well-evaluated fully quantitative software for bronchiectasis and air trapping (AT)., Results: CT bronchiectasis and AT occurred in 24 % and 58 % of patients, respectively. Of the functional tools, MBW detected the highest rates of abnormality: S cond 82 %, MBW TG RV 78 %, LCI 74 %, MBW TG IC 68 % and S acin 51 %. CPET VO 2 peak detected slightly higher rates of abnormality (9 %) than spirometry-based FEV 1 (2 %). For oscillometry AX (14 %) performed better than Rrs (2 %) whereas Xrs and R5-19 failed to detect any abnormality. LCI and S cond correlated with bronchiectasis (r = 0.55-0.64, p < 0.001) and AT (r = 0.73-0.74, p < 0.001). MBW-assessed trapped gas was detectable in 92 % of subjects and concordant with CT-assessed AT in 74 %., Conclusions: Significant structural and functional deficits occur in early CF lung disease, as detected by CT and MBW. For MBW, additional utility, beyond that offered by LCI, was suggested for S cond and MBW-assessed gas trapping. Our study reinforces the complementary nature of these tools and the limited utility of conventional oscillometry and CPET in this setting., Competing Interests: Declaration of competing interest The authors have no conflict of interests to declare, (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features., Methods: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS)., Results: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04 (pfs) /0.005 (tr) ), gradient short-run emphasis (p = 0.06 (pfs) /0.01 (tr) ), and zone percentage (p = 0.02 (pfs) /0.01 (tr) ) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01 (pfs) /0.04 (tr) ). Clustering of these features allows stratification of patients into groups of longer and shorter survival., Conclusion: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early., Critical Relevance Statement: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging., Key Points: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management., (© 2024. The Author(s).)

Introduction: The aim of this study was to apply quantitative computed tomography (QCT) for GOLD-grade specific disease characterization and phenotyping of air-trapping, emphysema, and airway abnormalities in patients with chronic obstructive pulmonary disease (COPD) from a nationwide cohort study., Methods: As part of the COSYCONET multicenter study, standardized CT in ex- and inspiration, lung function assessment (FEV1/FVC), and clinical scores (BODE index) were prospectively acquired in 525 patients (192 women, 327 men, aged 65.7 ± 8.5 years) at risk for COPD and at GOLD1-4. QCT parameters such as total lung volume (TLV), emphysema index (EI), parametric response mapping (PRM) for emphysema (PRMEmph) and functional small airway disease (PRMfSAD), total airway volume (TAV), wall percentage (WP), and total diameter (TD) were computed using automated software., Results: TLV, EI, PRMfSAD, and PRMEmph increased incrementally with each GOLD grade (p &lt; 0.001). Aggregated WP5-10 of subsegmental airways was higher from GOLD1 to GOLD3 and lower again at GOLD4 (p &lt; 0.001), whereas TD5-10 was significantly dilated only in GOLD4 (p &lt; 0.001). Fifty-eight patients were phenotyped as "non-airway non-emphysema type," 202 as "airway type," 96 as "emphysema type," and 169 as "mixed type." FEV1/FVC was best in "non-airway non-emphysema type" compared to other phenotypes, while "mixed type" had worst FEV1/FVC (p &lt; 0.001). BODE index was 0.56 ± 0.72 in the "non-airway non-emphysema type" and highest with 2.55 ± 1.77 in "mixed type" (p &lt; 0.001)., Conclusion: QCT demonstrates increasing hyperinflation and emphysema depending on the GOLD grade, while airway wall thickening increases until GOLD3 and airway dilatation occur in GOLD4. QCT identifies four disease phenotypes with implications for lung function and prognosis., (© 2024 S. Karger AG, Basel.)

Objectives: To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations., Materials and Methods: Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56-69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods., Results: The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases., Conclusion: Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis., Critical Relevance Statement: Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions., Key Points: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities., (© 2024. The Author(s).)

Objectives: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity., Materials and Methods: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM)., Results: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001)., Conclusion: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration., Clinical Relevance Statement: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping., Key Points: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001)., (© 2023. The Author(s).)

Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD., Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRM Emph , functional small-airway disease: PRM fSAD ) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated., Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRM Emph , PRM fSAD , anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages ( p  < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRM Emph , PRM fSAD , and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRM Emph ( r  = 0.66, p  < 0.01) and PRM fSAD ( r  = 0.61, p  < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters ( p  < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements., Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Almeida, Norajitra, Lüth, Wald, Weru, Nolden, Jäger, von Stackelberg, Heußel, Weinheimer, Biederer, Kauczor and Maier-Hein.)

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator ( CFTR ) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV 1 )] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis ( p = 0.281). Quantitatve WP ( p < 0.001) and WT ( p = 0.004) were reduced, conversely LA increased ( p = 0.003), and BI improved ( p = 0.012). Lung volume increased ( p < 0.001), and MLD decreased ( p < 0.001) through a reduction of ground glass opacity areas ( p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV 1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials., Competing Interests: The authors of this manuscript declare relationships with the following companies: SD reports grants from Siemens; personal honoraria for lectures from !DE Werbeagentur GmbH, Boehringer Ingelheim, med update GmbH. OW reports study funding and fees paid to his institution by Vertex Pharmaceuticals and Airway analysis technology licensed to Imbio, L.L.C. MW reports study funding and fees paid to his institution by Boehringer Ingelheim and Vertex Pharmaceuticals. JF reports personal fees/speaker honoraria from AstraZeneca outside the submitted work. TW reports grants from the German Ministry of Research and Education (BMBF) paid to his institution; personal honoraria for lectures from Insmed and personal payments for participation on an advisory board from Insmed. FW reports grants unrelated to this publication from the German Ministry of Research and Education (BMBF), German Cancer Aid, Siemens Healthineers, Promedicus and Delcath paid to his institution. TW reports grants from the German Ministry of Research and Education (BMBF) paid to his institution; personal honoraria for lectures from Insmed and personal payments for participation on an advisory board from Insmed. FR reports grants from the German Center for Lung Research (DZL), the German Center for Infection Research (DZIF), the Innovative Medicines Initiative (IMI; EU/EFPIA), and the iABC Consortium (including Alaxia, Basilea, Novartis, and Polyphor), Mukoviszidose Institute, Novartis, Insmed Germany, Grifols, Bayer Healthcare, and InfectoPharm paid to his institution; fees for clinical trial participation from Abbvie, AstraZeneca, Boehringer Ingelheim, Celtaxsys, Corbus, Insmed, Novartis, Parion, University of Dundee, Vertex, and Zambon paid to his institution; personal consulting fees from Parion Sciences, Grifols, Zambon, Insmed, and Helmholtz Center for Infection Research; personal honoraria for lectures from !DE Werbeagentur GmbH, Interkongress GmbH, AstraZeneca, Insmed, Grifols, University Hospital Frankfurt; payment for expert testimony from the Social Court Cologne; financial support for attending meetings from the German Kartagener Syndrome and Primary Ciliary Dyskinesia Patient Advisory Group, as well as the German Cystic Fibrosis Patient Advisory Group (Mukoviszidose e.V.); personal payments for participation on an advisory board from Insmed, Grifols, and Shionogi; as well as honorary (unpaid) commitment as coordinator of the ERNLUNG Bronchiectasis Core Network, cochair of the German Bronchiectasis Registry PROGNOSIS, member of the steering committee of the European Bronchiectasis Registry EMBARC, member of the steering committee of the European NTM Registry EMBARC-NTM, co-speaker of the Medical Advisory Board of the German Kartagener Syndrome and Primary Ciliary Dyskinesia Patient Advisory Group, speaker of the Respiratory Infections and TB group of the German Respiratory Society (DGP), speaker of the Cystic Fibrosis group of German Respiratory Society (DGP), principal investigator of the German Center for Lung Research (DZL), member of the Protocol Review Committee of the PCD Clinical Trial Network, and member of Physician Association of the German Cystic Fibrosis Patient Advisory Group (Mukoviszidose e.V.). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dettmer, Weinheimer, Sauer-Heilborn, Lammers, Wielpütz, Fuge, Welte, Wacker and Ringshausen.)