1
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Bayfield KJ, Weinheimer O, Middleton A, Boyton C, Fitzpatrick R, Kennedy B, Blaxland A, Jayasuriya G, Caplain N, Wielpütz MO, Yu L, Galban CJ, Robinson TE, Bartholmai B, Gustafsson P, Fitzgerald D, Selvadurai H, Robinson PD. Comparative sensitivity of early cystic fibrosis lung disease detection tools in school aged children. J Cyst Fibros 2024:S1569-1993(24)00075-4. [PMID: 38969602 DOI: 10.1016/j.jcf.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 05/05/2024] [Accepted: 05/20/2024] [Indexed: 07/07/2024]
Abstract
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 (FEV1≥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: Scond 82 %, MBWTG RV 78 %, LCI 74 %, MBWTG IC 68 % and Sacin 51 %. CPET VO2peak detected slightly higher rates of abnormality (9 %) than spirometry-based FEV1 (2 %). For oscillometry AX (14 %) performed better than Rrs (2 %) whereas Xrs and R5-19 failed to detect any abnormality. LCI and Scond 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 Scond 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.
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Affiliation(s)
- Katie J Bayfield
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg, German Center for Lung Research DZL, Heidelberg, Germany
| | - Anna Middleton
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Christie Boyton
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Rachel Fitzpatrick
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Brendan Kennedy
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Anneliese Blaxland
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Geshani Jayasuriya
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia; Woolcock Institute of Medical Research, Sydney, New South Wales, Australia
| | - Neil Caplain
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg, German Center for Lung Research DZL, Heidelberg, Germany
| | - Lifeng Yu
- Division of Radiology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Craig J Galban
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, USA
| | - Terry E Robinson
- Department of Pediatrics, Center of Excellence in Pulmonary Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Brian Bartholmai
- Division of Radiology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Per Gustafsson
- Department of Paediatrics, Central Hospital, Skövde, Sweden
| | - Dominic Fitzgerald
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia; The University of Sydney, Sydney, New South Wales, Australia
| | - Hiran Selvadurai
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia; The University of Sydney, Sydney, New South Wales, Australia
| | - Paul D Robinson
- The Children's Hospital at Westmead, Westmead, New South Wales, Australia; Woolcock Institute of Medical Research, Sydney, New South Wales, Australia; The University of Sydney, Sydney, New South Wales, Australia; Children's Health and Environment Program, Child Health Research Centre, University of Queensland, South Brisbane, Australia.
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2
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Mills DR, Masters IB, Yerkovich ST, McEniery J, Kapur N, Chang AB, Marchant JM, Goyal V. Radiographic Outcomes in Pediatric Bronchiectasis and Factors Associated with Reversibility. Am J Respir Crit Care Med 2024; 210:97-107. [PMID: 38631023 DOI: 10.1164/rccm.202402-0411oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/17/2024] [Indexed: 04/19/2024] Open
Abstract
Rationale: Conventionally considered irreversible, bronchiectasis has been demonstrated to be reversible in children in small studies. However, the factors associated with radiographic reversibility of bronchiectasis have yet to be defined. Objectives: In a large cohort of children with bronchiectasis, we aimed to determine: 1) if and to what extent bronchiectasis is reversible and 2) factors associated with radiographic chest high-resolution computed tomography (cHRCT) resolution. Methods: We identified children with bronchiectasis who had a repeat multidetector cHRCT scan between 2010 and 2021. We excluded those with cystic fibrosis, surgical pulmonary resection, traction bronchiectasis only, or lobar opacification. Measurements and Main Results: cHRCT scans were scored using the modified Reiff score (MRS) with a pediatric correction. Resolution was defined as an absence of abnormal bronchoarterial ratio (>0.8) on the second cHRCT scan. We included 142 children (median age, 5 years; IQR, 2.6-7.4). Inter- and intrarater agreement in MRSs was excellent (weighted κ = 0.83-0.86 and 0.95, respectively). There was radiographic resolution in 57 of 142 patients (40.1%), improvement in 56 of 142 (39.4%), and no change or worsening in 29 of 142 (20.4%). Pseudomonas aeruginosa (PsA) was absolutely associated with a lack of resolution. On multivariable regression, in those without PsA cultured, younger age at the time of diagnosis (risk ratio [RR], 0.94; 95% confidence interval [CI], 0.88-0.99), lower MRS (RR, 0.89; 95% CI, 0.82-0.97), and lower annual rate of exacerbations requiring intravenous antibiotic therapy (RR, 0.60; 95% CI, 0.37-0.98) increased the likelihood of radiographic resolution. Conclusions: This first large cohort confirms that bronchiectasis in children is often reversible with appropriate management. Younger children and those with lesser radiographic severity at diagnosis were most likely to exhibit radiographic reversibility, whereas those with PsA infection were least likely.
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Affiliation(s)
- Dustin R Mills
- Department of Respiratory and Sleep Medicine and
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
- Department of Pediatrics, Townsville University Hospital, Douglas, Queensland, Australia
| | - Ian B Masters
- Department of Respiratory and Sleep Medicine and
- National Health and Medical Research Council Centre for Research Excellence in Paediatric Bronchiectasis (AusBREATHE), Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Stephanie T Yerkovich
- National Health and Medical Research Council Centre for Research Excellence in Paediatric Bronchiectasis (AusBREATHE), Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Child and Maternal Health Division, Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia; and
| | - Jane McEniery
- Medical Imaging Nuclear Medicine, Queensland Children's Hospital, South Brisbane, Queensland, Australia
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Nitin Kapur
- Department of Respiratory and Sleep Medicine and
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Anne B Chang
- Department of Respiratory and Sleep Medicine and
- National Health and Medical Research Council Centre for Research Excellence in Paediatric Bronchiectasis (AusBREATHE), Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Child and Maternal Health Division, Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia; and
| | - Julie M Marchant
- Department of Respiratory and Sleep Medicine and
- National Health and Medical Research Council Centre for Research Excellence in Paediatric Bronchiectasis (AusBREATHE), Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Vikas Goyal
- Department of Respiratory and Sleep Medicine and
- National Health and Medical Research Council Centre for Research Excellence in Paediatric Bronchiectasis (AusBREATHE), Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Department of Paediatrics, Gold Coast University Hospital, Southport, Queensland, Australia
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3
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van den Bosch WB, Lv Q, Andrinopoulou ER, Pijnenburg MW, Ciet P, Janssens HM, Tiddens HA. Children with severe asthma have substantial structural airway changes on computed tomography. ERJ Open Res 2024; 10:00121-2023. [PMID: 38226065 PMCID: PMC10789264 DOI: 10.1183/23120541.00121-2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/17/2023] [Indexed: 01/17/2024] Open
Abstract
Background In adults with severe asthma (SA) bronchial wall thickening, bronchiectasis and low attenuation regions (LAR) have been described on chest computed tomography (CT) scans. The extent to which these structural abnormalities are present in children with SA is largely unknown. Our aim was to study the presence and extent of airway abnormalities on chest CT of children with SA. Methods 161 inspiratory and expiratory CT scans, either spirometer-controlled or technician-controlled, obtained in 131 children with SA (mean±SD age 11.0±3.8 years) were collected retrospectively. Inspiratory scans were analysed manually using a semi-quantitative score and automatically using LungQ (v2.1.0.1; Thirona B.V., Nijmegen, the Netherlands). LungQ segments the bronchial tree, identifies the generation for each bronchus-artery (BA) pair and measures the following BA dimensions: outer bronchial wall diameter (Bout), adjacent artery diameter (A) and bronchial wall thickness (Bwt). Bronchiectasis was defined as Bout/A ≥1.1, bronchial wall thickening as Bwt/A ≥0.14. LAR, reflecting small airways disease (SAD), was measured automatically on inspiratory and expiratory scans and manually on expiratory scans. Functional SAD was defined as FEF25-75 and/or FEF75 z-scores <-1.645. Results are shown as median and interquartile range. Results Bronchiectasis was present on 95.8% and bronchial wall thickening on all CTs using the automated method. Bronchiectasis was present on 28% and bronchial wall thickening on 88.8% of the CTs using the manual semi-quantitative analysis. The percentage of BA pairs defined as bronchiectasis was 24.62% (12.7-39.3%) and bronchial wall thickening was 41.7% (24.0-79.8%) per CT using the automated method. LAR was observed on all CTs using the automatic analysis and on 82.9% using the manual semi-quantitative analysis. Patients with LAR or functional SAD had more thickened bronchi than patients without. Conclusion Despite a large discrepancy between the automated and the manual semi-quantitative analysis, bronchiectasis and bronchial wall thickening are present on most CT scans of children with SA. SAD is related to bronchial wall thickening.
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Affiliation(s)
- Wytse B. van den Bosch
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
- Erasmus MC, University Medical Center Rotterdam, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
| | - Qianting Lv
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
- Erasmus MC, University Medical Center Rotterdam, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
| | - Eleni-Rosalina Andrinopoulou
- Erasmus MC, University Medical Center Rotterdam, Department of Biostatistics, Rotterdam, the Netherlands
- Erasmus MC, University Medical Center Rotterdam, Department of Epidemiology, Rotterdam, the Netherlands
| | - Mariëlle W.H. Pijnenburg
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
| | - Pierluigi Ciet
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
- Erasmus MC, University Medical Center Rotterdam, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
- Department of Radiology, Policlinico Universitario, University of Cagliari, Cagliari, Italy
| | - Hettie M. Janssens
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
| | - Harm A.W.M. Tiddens
- Erasmus MC – Sophia Children's Hospital, University Medical Center Rotterdam, Department of Paediatrics, division of Respiratory Medicine and Allergology, Rotterdam, the Netherlands
- Erasmus MC, University Medical Center Rotterdam, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
- Thirona BV, Nijmegen, the Netherlands
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4
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Tang Z, Nan Y, Walsh S, Yang G. Adversarial Transformer for Repairing Human Airway Segmentation. IEEE J Biomed Health Inform 2023; 27:5015-5022. [PMID: 37379175 DOI: 10.1109/jbhi.2023.3290136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Automated airway segmentation models often suffer from discontinuities in peripheral bronchioles, which limits their clinical applicability. Furthermore, data heterogeneity across different centres and pathological abnormalities pose significant challenges to achieving accurate and robust segmentation in distal small airways. Accurate segmentation of airway structures is essential for the diagnosis and prognosis of lung diseases. To address these issues, we propose a patch-scale adversarial-based refinement network that takes in preliminary segmentation and original CT images and outputs a refined mask of the airway structure. Our method is validated on three datasets, including healthy cases, pulmonary fibrosis, and COVID-19 cases, and quantitatively evaluated using seven metrics. Our method achieves more than a 15% increase in the detected length ratio and detected branch ratio compared to previously proposed models, demonstrating its promising performance. The visual results show that our refinement approach, guided by a patch-scale discriminator and centreline objective functions, effectively detects discontinuities and missing bronchioles. We also demonstrate the generalizability of our refinement pipeline on three previous models, significantly improving their segmentation completeness. Our method provides a robust and accurate airway segmentation tool that can help improve diagnosis and treatment planning for lung diseases.
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5
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Díaz AA, Nardelli P, Wang W, San José Estépar R, Yen A, Kligerman S, Maselli DJ, Dolliver WR, Tsao A, Orejas JL, Aliberti S, Aksamit TR, Young KA, Kinney GL, Washko GR, Silverman EK, San José Estépar R. Artificial Intelligence-based CT Assessment of Bronchiectasis: The COPDGene Study. Radiology 2023; 307:e221109. [PMID: 36511808 PMCID: PMC10068886 DOI: 10.1148/radiol.221109] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/28/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Background CT is the standard method used to assess bronchiectasis. A higher airway-to-artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent of this metric in CT scans. Purpose To determine the extent of AARs using an artificial intelligence-based chest CT and assess the association of AARs with exacerbations over time. Materials and Methods In a secondary analysis of ever-smokers from the prospective, observational, multicenter COPDGene study, AARs were quantified using an artificial intelligence tool. The percentage of airways with AAR greater than 1 (a measure of airway dilatation) in each participant on chest CT scans was determined. Pulmonary exacerbations were prospectively determined through biannual follow-up (from July 2009 to September 2021). Multivariable zero-inflated regression models were used to assess the association between the percentage of airways with AAR greater than 1 and the total number of pulmonary exacerbations over follow-up. Covariates included demographics, lung function, and conventional CT parameters. Results Among 4192 participants (median age, 59 years; IQR, 52-67 years; 1878 men [45%]), 1834 had chronic obstructive pulmonary disease (COPD). During a 10-year follow-up and in adjusted models, the percentage of airways with AARs greater than 1 (quartile 4 vs 1) was associated with a higher total number of exacerbations (risk ratio [RR], 1.08; 95% CI: 1.02, 1.15; P = .01). In participants meeting clinical and imaging criteria of bronchiectasis (ie, clinical manifestations with ≥3% of AARs >1) versus those who did not, the RR was 1.37 (95% CI: 1.31, 1.43; P < .001). Among participants with COPD, the corresponding RRs were 1.10 (95% CI: 1.02, 1.18; P = .02) and 1.32 (95% CI: 1.26, 1.39; P < .001), respectively. Conclusion In ever-smokers with chronic obstructive pulmonary disease, artificial intelligence-based CT measures of bronchiectasis were associated with more exacerbations over time. Clinical trial registration no. NCT00608764 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Schiebler and Seo in this issue.
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Affiliation(s)
- Alejandro A. Díaz
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Pietro Nardelli
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Wei Wang
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Rubén San José Estépar
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Andrew Yen
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Seth Kligerman
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Diego J. Maselli
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Wojciech R. Dolliver
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Andrew Tsao
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - José L. Orejas
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Stefano Aliberti
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Timothy R. Aksamit
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Kendra A. Young
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Gregory L. Kinney
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - George R. Washko
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Edwin K. Silverman
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
| | - Raúl San José Estépar
- From the Division of Pulmonary and Critical Care Medicine (A.A.D.,
W.R.D., A.T., J.L.O., G.R.W.), Department of Radiology (P.N., Rubén San
José Estépar, Raúl San José Estépar),
Division of Sleep Medicine and Circadian Disorders (W.W.), and Channing Division
of Network Medicine (E.K.S.), Brigham and Women’s Hospital, Harvard
Medical School, 15 Francis St, Boston, MA 02115; Department of Radiology,
University of California–San Diego, San Diego, Calif (A.Y., S.K.);
Division of Pulmonary Diseases and Critical Care, University of Texas–San
Antonio, San Antonio, Tex (D.J.M.); Department of Biomedical Sciences, Humanitas
University, Milan, Italy (S.A.); Respiratory Unit, IRCCS Humanitas Research
Hospital, Milan, Italy (S.A.); Department of Pulmonary Disease and Critical Care
Medicine, Mayo Clinic, Rochester, Minn (T.R.A.); and Department of Epidemiology,
Colorado School of Public Health, University of Colorado, Aurora, Colo (K.A.Y.,
G.L.K.)
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6
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Flume PA, Basavaraj A, Garcia B, Winthrop K, Di Mango E, Daley CL, Philley JV, Henkle E, O'Donnell AE, Metersky M. Towards development of evidence to inform recommendations for the evaluation and management of bronchiectasis. Respir Med 2023; 211:107217. [PMID: 36931575 DOI: 10.1016/j.rmed.2023.107217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/17/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
Bronchiectasis (BE) is a chronic condition characterized by airway dilation as a consequence of a variety of pathogenic processes. It is often associated with persistent airway infection and an inflammatory response resulting in cough productive of purulent sputum, which has an adverse impact on quality of life. The prevalence of BE is increasing worldwide. Treatment guidelines exist for managing BE, but they are generally informed by a paucity of high-quality evidence. This review presents the findings of a scientific advisory board of experts held in the United States in November 2020. The main focus of the meeting was to identify unmet needs in BE and propose ways to identify research priorities for the management of BE, with a view to developing evidence-based treatment recommendations. Key issues identified include diagnosis, patient evaluation, promoting airway clearance and appropriate use of antimicrobials. Unmet needs include effective pharmacological agents to promote airway clearance and reduce inflammation, control of chronic infection, clinical endpoints to be used in the design of BE clinical trials, and more accurate classification of patients using phenotypes and endotypes to better guide treatment decisions and improve outcomes.
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Affiliation(s)
- Patrick A Flume
- Department of Medicine and Pediatrics, Medical University of South Carolina, 96 Jonathan Lucas Street, Room 816-CSB, Charleston, SC, USA.
| | - Ashwin Basavaraj
- Division of Pulmonary, Critical Care and Sleep Medicine, New York University Grossman School of Medicine, 462 First Avenue, Administration Building OBV, A601, New York, NY, 10016, USA.
| | - Bryan Garcia
- University of Alabama at Birmingham, 1900 University Blvd, THT Suite 541A, Birmingham, AL, 35233, USA.
| | - Kevin Winthrop
- Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, 97239, Portland, OR, USA.
| | - Emily Di Mango
- Department of Medicine, Columbia University Irving Medical Center, 622 West 168th Street, New York, NY, 10032, USA.
| | - Charles L Daley
- Department of Medicine, National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA.
| | - Julie V Philley
- Division of Pulmonary and Critical Care Medicine, University of Texas Health Science Center at Tyler, 11937 US Hwy 271, 75708, Tyler, USA.
| | - Emily Henkle
- Oregon Health and Science University, OHSU-PSU School of Public Health, 3181 SW Sam Jackson Park Rd, Mailcode VPT, Portland, OR, 97239, USA.
| | - Anne E O'Donnell
- Division of Pulmonary, Critical Care and Sleep Medicine, Georgetown University Medical Center, Washington, DC, USA.
| | - Mark Metersky
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT, 06030-1321, USA.
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7
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Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases. Biomedicines 2023; 11:biomedicines11010133. [PMID: 36672641 PMCID: PMC9855445 DOI: 10.3390/biomedicines11010133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images.
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8
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Mok LC, Garcia-Uceda A, Cooper MN, Kemner-Van De Corput M, De Bruijne M, Feyaerts N, Rosenow T, De Boeck K, Stick S, Tiddens HAWM. The effect of CFTR modulators on structural lung disease in cystic fibrosis. Front Pharmacol 2023; 14:1147348. [PMID: 37113757 PMCID: PMC10127680 DOI: 10.3389/fphar.2023.1147348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/23/2023] [Indexed: 04/29/2023] Open
Abstract
Background: Newly developed quantitative chest computed tomography (CT) outcomes designed specifically to assess structural abnormalities related to cystic fibrosis (CF) lung disease are now available. CFTR modulators potentially can reduce some structural lung abnormalities. We aimed to investigate the effect of CFTR modulators on structural lung disease progression using different quantitative CT analysis methods specific for people with CF (PwCF). Methods: PwCF with a gating mutation (Ivacaftor) or two Phe508del alleles (lumacaftor-ivacaftor) provided clinical data and underwent chest CT scans. Chest CTs were performed before and after initiation of CFTR modulator treatment. Structural lung abnormalities on CT were assessed using the Perth Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF), airway-artery dimensions (AA), and CF-CT methods. Lung disease progression (0-3 years) in exposed and matched unexposed subjects was compared using analysis of covariance. To investigate the effect of treatment in early lung disease, subgroup analyses were performed on data of children and adolescents aged <18 years. Results: We included 16 modulator exposed PwCF and 25 unexposed PwCF. Median (range) age at the baseline visit was 12.55 (4.25-36.49) years and 8.34 (3.47-38.29) years, respectively. The change in PRAGMA-CF %Airway disease (-2.88 (-4.46, -1.30), p = 0.001) and %Bronchiectasis extent (-2.07 (-3.13, -1.02), p < 0.001) improved in exposed PwCF compared to unexposed. Subgroup analysis of paediatric data showed that only PRAGMA-CF %Bronchiectasis (-0.88 (-1.70, -0.07), p = 0.035) improved in exposed PwCF compared to unexposed. Conclusion: In this preliminary real-life retrospective study CFTR modulators improve several quantitative CT outcomes. A follow-up study with a large cohort and standardization of CT scanning is needed to confirm our findings.
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Affiliation(s)
- L. Clara Mok
- Faculty of Medicine and Health Sciences, The University of Western Australia, Perth, WA, Australia
- Telethon Kids Institute, The University of Western Australia, Nedlands, WA, Australia
| | - Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Pediatric Pulmonology and Allergology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, Netherlands
| | - Matthew N. Cooper
- Telethon Kids Institute, The University of Western Australia, Nedlands, WA, Australia
| | | | - Marleen De Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Nathalie Feyaerts
- Department of Pediatric Pulmonology, University of Leuven, Leuven, Belgium
| | - Tim Rosenow
- Faculty of Medicine and Health Sciences, The University of Western Australia, Perth, WA, Australia
- Telethon Kids Institute, The University of Western Australia, Nedlands, WA, Australia
| | - Kris De Boeck
- Department of Pediatric Pulmonology, University of Leuven, Leuven, Belgium
| | - Stephen Stick
- Faculty of Medicine and Health Sciences, The University of Western Australia, Perth, WA, Australia
- Telethon Kids Institute, The University of Western Australia, Nedlands, WA, Australia
- Department of Respiratory Medicine, Perth Children’s Hospital, Perth, WA, Australia
| | - Harm A. W. M. Tiddens
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Pediatric Pulmonology and Allergology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, Netherlands
- *Correspondence: Harm A. W. M. Tiddens,
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9
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Aliboni L, Pennati F, Gelmini A, Colombo A, Ciuni A, Milanese G, Sverzellati N, Magnani S, Vespro V, Blasi F, Aliverti A, Aliberti S. Detection and Classification of Bronchiectasis Through Convolutional Neural Networks. J Thorac Imaging 2022; 37:100-108. [PMID: 33758127 DOI: 10.1097/rti.0000000000000588] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE Bronchiectasis is a chronic disease characterized by an irreversible dilatation of bronchi leading to chronic infection, airway inflammation, and progressive lung damage. Three specific patterns of bronchiectasis are distinguished in clinical practice: cylindrical, varicose, and cystic. The predominance and the extension of the type of bronchiectasis provide important clinical information. However, characterization is often challenging and is subject to high interobserver variability. The aim of this study is to provide an automatic tool for the detection and classification of bronchiectasis through convolutional neural networks. MATERIALS AND METHODS Two distinct approaches were adopted: (i) direct network performing a multilabel classification of 32×32 regions of interest (ROIs) into 4 classes: healthy, cylindrical, cystic, and varicose and (ii) a 2-network serial approach, where the first network performed a binary classification between normal tissue and bronchiectasis and the second one classified the ROIs containing abnormal bronchi into one of the 3 bronchiectasis typologies. Performances of the networks were compared with other architectures presented in the literature. RESULTS Computed tomography from healthy individuals (n=9, age=47±6, FEV1%pred=109±17, FVC%pred=116±17) and bronchiectasis patients (n=21, age=59±15, FEV1%pred=74±25, FVC%pred=91±22) were collected. A total of 19,059 manually selected ROIs were used for training and testing. The serial approach provided the best results with an accuracy and F1 score average of 0.84, respectively. Slightly lower performances were observed for the direct network (accuracy=0.81 and F1 score average=0.82). On the test set, cylindrical bronchiectasis was the subtype classified with highest accuracy, while most of the misclassifications were related to the varicose pattern, mainly to the cylindrical class. CONCLUSION The developed networks accurately detect and classify bronchiectasis disease, allowing to collect quantitative information regarding the radiologic severity and the topographical distribution of bronchiectasis subtype.
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Affiliation(s)
- Lorenzo Aliboni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano
| | - Alice Gelmini
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano
| | - Alessandra Colombo
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano
| | - Andrea Ciuni
- Department of Clinical Sciences, Section of Radiology, University of Parma, Parma
| | - Gianluca Milanese
- Department of Clinical Sciences, Section of Radiology, University of Parma, Parma
| | - Nicola Sverzellati
- Department of Clinical Sciences, Section of Radiology, University of Parma, Parma
| | - Sandro Magnani
- Department of Radiology, ASST Lodi, Ospedale Maggiore di Lodi, Lodi, Italy
| | - Valentina Vespro
- Department of Radiology, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico Milan, University of Milan, Milan
| | - Francesco Blasi
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano
| | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano
| | - Stefano Aliberti
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano
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10
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Pakzad A, Jacob J. Radiology of Bronchiectasis. Clin Chest Med 2022; 43:47-60. [PMID: 35236560 DOI: 10.1016/j.ccm.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Bronchiectasis is a radiological diagnosis made using computed tomographic (CT) imaging. Although visual CT assessment is necessary for the diagnosis of bronchiectasis, visual assessment of disease severity and progression is challenging. Computer tools offer the potential to improve the characterization of lung damage in patients with bronchiectasis. Newer imaging techniques such as MRI with hyperpolarized gas inhalation have the potential to identify early forms of disease and are without the constraints of requiring ionizing radiation exposure.
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Affiliation(s)
- Ashkan Pakzad
- Departments of Medical Physics and Biomedical Engineering, and Computer Science, University College London, UK; Centre for Medical Image Computing, University College London, London, UK.
| | - Joseph Jacob
- Centre for Medical Image Computing, University College London, London, UK; UCL Respiratory, University College London, London, UK
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11
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Garcia-Uceda A, Selvan R, Saghir Z, Tiddens HAWM, de Bruijne M. Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks. Sci Rep 2021; 11:16001. [PMID: 34362949 PMCID: PMC8346579 DOI: 10.1038/s41598-021-95364-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/21/2021] [Indexed: 12/11/2022] Open
Abstract
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT'09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT'09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.
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Affiliation(s)
- Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands.
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Zaigham Saghir
- Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Copenhagen University Hospital, 2900, Hellerup, Denmark
| | - Harm A W M Tiddens
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark.
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12
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Ledda RE, Balbi M, Milone F, Ciuni A, Silva M, Sverzellati N, Milanese G. Imaging in non-cystic fibrosis bronchiectasis and current limitations. BJR Open 2021; 3:20210026. [PMID: 34381953 PMCID: PMC8328081 DOI: 10.1259/bjro.20210026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 01/21/2023] Open
Abstract
Non-cystic fibrosis bronchiectasis represents a heterogenous spectrum of disorders characterised by an abnormal and permanent dilatation of the bronchial tree associated with respiratory symptoms. To date, diagnosis relies on computed tomography (CT) evidence of dilated airways. Nevertheless, definite radiological criteria and standardised CT protocols are still to be defined. Although largely used, current radiological scoring systems have shown substantial drawbacks, mostly failing to correlate morphological abnormalities with clinical and prognostic data. In limited cases, bronchiectasis morphology and distribution, along with associated CT features, enable radiologists to confidently suggest an underlying cause. Quantitative imaging analyses have shown a potential to overcome the limitations of the current radiological criteria, but their application is still limited to a research setting. In the present review, we discuss the role of imaging and its current limitations in non-cystic fibrosis bronchiectasis. The potential of automatic quantitative approaches and artificial intelligence in such a context will be also mentioned.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Maurizio Balbi
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Francesca Milone
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Andrea Ciuni
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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13
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Wu J, Bracken J, Lam A, Francis KL, Ramanauskas F, Chang AB, Robinson P, McCallum P, Wurzel DF. Refining diagnostic criteria for paediatric bronchiectasis using low-dose CT scan. Respir Med 2021; 187:106547. [PMID: 34340172 DOI: 10.1016/j.rmed.2021.106547] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 06/10/2021] [Accepted: 07/21/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND There is a current lack of consensus amongst paediatric radiologists and respiratory paediatricians as to the correct CT definition of bronchiectasis in children. Using contemporary low-dose CT, our objectives were to determine the upper limit of normal for broncho-arterial ratio (BAR) in children and to evaluate the effect of age and general anaesthesia. METHODS Measurements of 330 broncho-arterial ratios from 51 children (0-19 years) undergoing low-dose CT chest for non-respiratory indications were performed by 3 blinded observers (two radiologists, one respiratory physician) using four different methods. Inter-observer reliability, mean BAR and reference ranges (mean±2SD) were calculated. Correlation between age and BARs were examined. Mean BAR for CT under general anaesthesia and CT awake were compared. RESULTS Inter-observer correlation was extremely high for all measurements (0.93-0.97). There was a weak positive correlation between age and BAR in the CT-awake group (r = 0.33, 95%CI: 0.03-0.57; p = 0.031) using the inner-bronchial wall to artery, short-axis measurement. CT under general anaesthesia showed significantly higher BAR compared to CT-awake [mean difference 0.13 (95%CI: 0.05-0.22; p = 0.004)]. For the CT-awake group, the mean BAR was 0.65 (range: 0.42 to 0.89), with no child having a BAR above 0.9. CONCLUSION Using a standardised approach, we have shown that a broncho-arterial ratio above 0.9 in children undergoing awake CT is abnormal and suggests airway widening or radiological bronchiectasis. Children undergoing CT under anaesthesia have higher BARs than those undergoing awake CT. A weak positive correlation between broncho-arterial ratio and age was observed, hence, age-adjusted cut-offs for BAR warrant further study.
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Affiliation(s)
- Johnny Wu
- Department of Respiratory and Sleep Medicine, The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Jennifer Bracken
- Department of Medical Imaging, The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Adrienne Lam
- Department of Medical Imaging, The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Kate L Francis
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, VIC, Australia
| | - Fiona Ramanauskas
- Department of Medical Imaging, The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Anne B Chang
- Australian Centre for Health Services Innovation, Queensland University of Technology, Brisbane, QLD, Australia; Child Health Division, Menzies School of Health Research, Darwin, NT, Australia
| | - Phil Robinson
- Department of Respiratory and Sleep Medicine, The Royal Children's Hospital, Melbourne, VIC, Australia; Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
| | - Paul McCallum
- Department of Anaesthesia and Pain Management, The Royal Children's Hospital, Melbourne, VIC, Australia
| | - Danielle F Wurzel
- Department of Respiratory and Sleep Medicine, The Royal Children's Hospital, Melbourne, VIC, Australia; Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia.
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14
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Amati F, Simonetta E, Pilocane T, Gramegna A, Goeminne P, Oriano M, Pascual-Guardia S, Mantero M, Voza A, Santambrogio M, Blasi F, Aliberti S. Diagnosis and Initial Investigation of Bronchiectasis. Semin Respir Crit Care Med 2021; 42:513-524. [PMID: 34261176 DOI: 10.1055/s-0041-1730892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Bronchiectasis refers to both the name of a disease and a single radiological appearance that may, or may not, be associated with disease. As chronic respiratory disease, bronchiectasis is characterized by a variable range of signs and symptoms that may overlap with other chronic respiratory conditions. The proper identification of bronchiectasis as a disease in both primary and secondary care is of paramount importance. However, a standardized definition of radiologically and clinically significant bronchiectasis is still missing. Disease heterogeneity is a hallmark of bronchiectasis and applies not only to radiological features and clinical manifestations but also to other aspects of the disease, including the etiological and microbiological diagnosis as well as the evaluation of pulmonary function. Although the guidelines suggest a "minimum bundle" of tests, the diagnostic approach to bronchiectasis is challenging and may be driven by the "treatable traits" approach based on endotypes and biological characteristics. A broad spectrum of diagnostic tests could be used to investigate the etiology of bronchiectasis as well as other pulmonary, extrapulmonary, and environmental traits. Individualizing bronchiectasis workup according to the site of care (e.g., primary, secondary, and tertiary care) could help optimize patients' management and reduce healthcare costs.
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Affiliation(s)
- Francesco Amati
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Edoardo Simonetta
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Tommaso Pilocane
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Andrea Gramegna
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Pieter Goeminne
- Department of Respiratory Medicine, AZ Nikolaas, Sint-Niklaas, Belgium
| | - Martina Oriano
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Sergi Pascual-Guardia
- Department of Respiratory Medicine, Hospital del Mar (PSMAR)-IMIM, Barcelona, Spain.,School of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.,CIBER, Área de Enfermedades Respiratorias (CIBERES), ISCIII, Spain
| | - Marco Mantero
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Antonio Voza
- Emergency Department, Humanitas Clinical and Research Center, IRCCS, Milan, Italy
| | - Martina Santambrogio
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Francesco Blasi
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stefano Aliberti
- Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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15
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Goralski JL, Stewart NJ, Woods JC. Novel imaging techniques for cystic fibrosis lung disease. Pediatr Pulmonol 2021; 56 Suppl 1:S40-S54. [PMID: 32592531 PMCID: PMC7808406 DOI: 10.1002/ppul.24931] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 06/25/2020] [Indexed: 12/24/2022]
Abstract
With an increasing number of patients with cystic fibrosis (CF) receiving highly effective CFTR (cystic fibrosis transmembrane regulator protein) modulator therapy, particularly at a young age, there is an increasing need to identify imaging tools that can detect and regionally visualize mild CF lung disease and subtle changes in disease state. In this review, we discuss the latest developments in imaging modalities for both structural and functional imaging of the lung available to CF clinicians and researchers, from the widely available, clinically utilized imaging methods for assessing CF lung disease-chest radiography and computed tomography-to newer techniques poised to become the next phase of clinical tools-structural/functional proton and hyperpolarized gas magnetic resonance imaging (MRI). Finally, we provide a brief discussion of several newer lung imaging techniques that are currently available only in selected research settings, including chest tomosynthesis, and fluorinated gas MRI. We provide an update on the clinical and/or research status of each technique, with a focus on sensitivity, early disease detection, and possibilities for monitoring treatment efficacy.
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Affiliation(s)
- Jennifer L Goralski
- UNC Cystic Fibrosis Center, Marsico Lung Institute, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Division of Pulmonary and Critical Care Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Division of Pediatric Pulmonology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Neil J Stewart
- Center for Pulmonary Imaging Research, Cincinnati Children's Hospital, Cincinnati, Ohio.,Department of Infection, Immunity & Cardiovascular Disease, POLARIS Group, Imaging Sciences, University of Sheffield, Sheffield, UK
| | - Jason C Woods
- Center for Pulmonary Imaging Research, Cincinnati Children's Hospital, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.,Department of Radiology, Cincinnati Children's Hospital, Cincinnati, Ohio
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16
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Kahnert K, Jörres RA, Kauczor HU, Biederer J, Jobst B, Alter P, Biertz F, Mertsch P, Lucke T, Lutter JI, Trudzinski FC, Behr J, Bals R, Watz H, Vogelmeier CF, Welte T. Relationship between clinical and radiological signs of bronchiectasis in COPD patients: Results from COSYCONET. Respir Med 2020; 172:106117. [PMID: 32891937 DOI: 10.1016/j.rmed.2020.106117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/07/2020] [Accepted: 08/09/2020] [Indexed: 01/01/2023]
Abstract
Bronchiectasis (BE) might be frequently present in COPD but masked by COPD symptoms. We studied the relationship of clinical signs of bronchiectasis to the presence and extent of its radiological signs in patients of different COPD severity. Visit 4 data (GOLD grades 1-4) of the COSYCONET cohort was used. Chest CT scans were evaluated for bronchiectasis in 6 lobes using a 3-point scale (0: absence, 1: ≤50%, 2: >50% BE-involvement for each lobe). 1176 patients were included (61%male, age 67.3y), among them 38 (3.2%) with reported physicians' diagnosis of bronchiectasis and 76 (6.5%) with alpha1-antitrypsin deficiency (AA1D). CT scans were obtained in 429 patients. Within this group, any signs of bronchiectasis were found in 46.6% of patients, whereby ≤50% BE occurred in 18.6% in ≤2 lobes, in 10.0% in 3-4 lobes, in 15.9% in 5-6 lobes; >50% bronchiectasis in at least 1 lobe was observed in 2.1%. Scores ≥4 correlated with an elevated ratio FRC/RV. The clinical diagnosis of bronchiectasis correlated with phlegm and cough and with radiological scores of at least 3, optimally ≥5. In COPD patients, clinical diagnosis and radiological signs of BE showed only weak correlations. Correlations became significant with increasing BE-severity implying radiological alterations in several lobes. This indicates the importance of reporting both presence and extent of bronchiectasis on CT. Further research is warranted to refine the criteria for CT scoring of bronchiectasis and to determine the relevance of radiologically but not clinically detectible bronchiectasis and their possible implications for therapy in COPD patients.
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Affiliation(s)
- Kathrin Kahnert
- Department of Internal Medicine V, University of Munich (LMU), Comprehensive Pneumology Center, Member of the German Center for Lung Research, Ziemssenstr. 1, 80336, Munich, Germany.
| | - Rudolf A Jörres
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Comprehensive Pneumology Center Munich (CPC-M), Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 80336, Munich, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; Translational Lung Research Centre Heidelberg (TLRC), Member of the German Center for Lung Research, Heidelberg, Germany; University of Latvia, Faculty of Medicine, Raina bulvaris 19, Riga, LV-1586, Latvia
| | - Jürgen Biederer
- Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; Translational Lung Research Centre Heidelberg (TLRC), Member of the German Center for Lung Research, Heidelberg, Germany; University of Latvia, Faculty of Medicine, Raina bulvaris 19, Riga, LV-1586, Latvia; Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, D-24098, Kiel, Germany
| | - Bertram Jobst
- Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; Translational Lung Research Centre Heidelberg (TLRC), Member of the German Center for Lung Research, Heidelberg, Germany
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-University Marburg, Germany, Member of the German Center for Lung Research (DZL), Baldingerstrasse, 35043, Marburg, Germany
| | - Frank Biertz
- Institute for Biostatistics, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Pontus Mertsch
- Department of Internal Medicine V, University of Munich (LMU), Comprehensive Pneumology Center, Member of the German Center for Lung Research, Ziemssenstr. 1, 80336, Munich, Germany
| | - Tanja Lucke
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Comprehensive Pneumology Center Munich (CPC-M), Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 80336, Munich, Germany
| | - Johanna I Lutter
- Institute of Epidemiology, Helmholtz Zentrum München (GmbH) - German Research Center for Environmental Health, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research (DZL), 85764, Neuherberg, Germany
| | | | - Jürgen Behr
- Department of Internal Medicine V, University of Munich (LMU), Comprehensive Pneumology Center, Member of the German Center for Lung Research, Ziemssenstr. 1, 80336, Munich, Germany
| | - Robert Bals
- Department of Internal Medicine V - Pulmonology, Allergology, Respiratory Intensive Care Medicine, Saarland University Hospital, Kirrberger Straße 1, 66424, Homburg, Germany
| | - Henrik Watz
- Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Woehrendamm 80, 22927, Grosshansdorf, Germany
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-University Marburg, Germany, Member of the German Center for Lung Research (DZL), Baldingerstrasse, 35043, Marburg, Germany
| | - Tobias Welte
- Department of Pneumology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
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17
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Tiddens HAWM, Meerburg JJ, van der Eerden MM, Ciet P. The radiological diagnosis of bronchiectasis: what's in a name? Eur Respir Rev 2020; 29:29/156/190120. [PMID: 32554759 PMCID: PMC9489191 DOI: 10.1183/16000617.0120-2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/02/2020] [Indexed: 12/31/2022] Open
Abstract
Diagnosis of bronchiectasis is usually made using chest computed tomography (CT) scan, the current gold standard method. A bronchiectatic airway can show abnormal widening and thickening of its airway wall. In addition, it can show an irregular wall and lack of tapering, and/or can be visible in the periphery of the lung. Its diagnosis is still largely expert based. More recently, it has become clear that airway dimensions on CT and therefore the diagnosis of bronchiectasis are highly dependent on lung volume. Hence, control of lung volume is required during CT acquisition to standardise the evaluation of airways. Automated image analysis systems are in development for the objective analysis of airway dimensions and for the diagnosis of bronchiectasis. To use these systems, clear and objective definitions for the diagnosis of bronchiectasis are needed. Furthermore, the use of these systems requires standardisation of CT protocols and of lung volume during chest CT acquisition. In addition, sex- and age-specific reference values are needed for image analysis outcome parameters. This review focusses on today's issues relating to the radiological diagnosis of bronchiectasis using state-of-the-art CT imaging techniques. Bronchiectasis diagnosis is expert based. Clear definitions, standardisation of lung volume and CT protocols, and reference values are needed to allow automated image analysis for its diagnosis and to be used for clinical management and clinical studies.http://bit.ly/35vASqz
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Affiliation(s)
- Harm A W M Tiddens
- Dept of Paediatric Pulmonology and Allergology, Erasmus Medical Centre (MC)-Sophia Children's Hospital, Rotterdam, The Netherlands .,Dept of Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Jennifer J Meerburg
- Dept of Paediatric Pulmonology and Allergology, Erasmus Medical Centre (MC)-Sophia Children's Hospital, Rotterdam, The Netherlands.,Dept of Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | | | - Pierluigi Ciet
- Dept of Paediatric Pulmonology and Allergology, Erasmus Medical Centre (MC)-Sophia Children's Hospital, Rotterdam, The Netherlands.,Dept of Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
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