Unlocking the Power of Low-Dose CT: Bronchial Parameters as Emerging Biomarkers in Pulmonary Disease

HomeRadiologyVol. 311, No. 3 PreviousNext Reviews and CommentaryFree AccessEditorialUnlocking the Power of Low-Dose CT: Bronchial Parameters as Emerging Biomarkers in Pulmonary DiseaseTilman Emrich , Akos Varga-SzemesTilman Emrich , Akos Varga-SzemesAuthor AffiliationsFrom the Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Langenbeckstr. 1, 55131 Mainz, Germany (T.E.); Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (T.E., A.V.S.); and German Centre for Cardiovascular Research, Mainz, Germany (T.E.).Address correspondence to T.E. (email: [email protected]).Tilman Emrich Akos Varga-SzemesPublished Online:Jun 25 2024https://doi.org/10.1148/radiol.241381See also the article by Dudurych et al in this issue.MoreSectionsPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In See also the article by Dudurych et al in this issue.Dr Tilman Emrich is an attending radiologist specializing in cardiothoracic imaging at the University Medical Center Mainz, Germany. He is also an adjunct assistant professor of radiology in the Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina (Charleston, SC). His main scientific interests focus on photon-counting detector CT for cardiothoracic applications and multiparametric cardiac imaging in a general population and disease cohorts.Download as PowerPointDr Akos Varga-Szemes is a professor of radiology and director of cardiovascular imaging research in the Department of Radiology and Radiological Science, Medical University of South Carolina. He is a physician scientist with a PhD degree in cardiothoracic imaging, more than 200 peer-reviewed publications, and more than 350 scientific abstracts. His primary research interests include myocardial tissue characterization, unenhanced MR angiography techniques, and cardiothoracic applications of photon-counting detector CT.Download as PowerPointChanges in certain CT-derived bronchial parameters such as bronchial luminal area and wall thickness have been linked to various pulmonary abnormalities including chronic obstructive pulmonary disease, asthma, and interstitial lung disease. Such parameters can be derived from low-dose chest CT, including lung cancer screening examinations, with high reproducibility (1). Therefore, CT-based bronchial parameters may be potential diagnostic or screening tools in general populations or in at-risk populations to help detect untreated or undertreated pulmonary diseases at an early stage. They may drive and support therapeutic interventions such as timely smoking cessation, and may be used as noninvasive imaging biomarkers of treatment response (2).To use these imaging biomarkers as a routine clinical tool, normal reference values in large-scale lung-healthy cohorts are required. Previous investigations have attempted to establish these reference values, but study cohorts were dominated by patients at high risk for or who were already known to have pulmonary diseases and therefore were potentially not representative of a general, lung-healthy population. In addition, physiologic differences (eg, sex and age) may influence the normal values of these quantitative parameters; however, such influence has been scarcely investigated (3). These issues lead to conflicting evidence of normal values and reference ranges for CT-derived bronchial parameters, ultimately limiting their clinical application as a diagnostic or screening tool (4).In this issue of Radiology, Dudurych et al (5) address this unmet need in a large subcohort from the multidisciplinary, prospective, population-based, longitudinal Imaging in Lifelines cohort study, which focuses on the derivation of imaging biomarkers from low-dose CT examinations in the general population. In this study, the authors proposed reference ranges for bronchial parameters based on low-dose chest CT scans in more than 8800 participants aged 45 years or older after careful exclusion of those with abnormal spirometry, self-reported respiratory disease, or morphologic signs of lung damage on CT scans. The cohort was further stratified by smoking status, including a large subgroup of never-smokers (41.4%). By using a recently validated, customized, artificial intelligence–based pipeline with rigorous quality control, the authors calculated quantitative imaging parameters of the bronchial system by segmenting the wall and lumen of the airways. These parameters included luminal area, wall thickness, wall area percentage, and the square root of the bronchial wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10).Key results indicated sex differences in bronchial parameters, with male participants having thicker bronchial walls (mean, 1.03 mm ± 0.05 vs 0.98 mm ± 0.05; P < .001), wider bronchial luminal areas (mean, 29.76 mm2 ± 5.79 vs 25.40 mm2 ± 4.66; P < .001), and higher Pi10 (mean, 3.66 mm ± 0.14 vs 3.49 mm ± 0.13; P < .001). Wall area percentage, however, was demonstrated to be lower in male participants (mean, 46.96% ± 3.42 vs 47.50% ± 3.29; P < .001). This relationship remained independent after correcting for age, height, weight, and smoking status. In addition, age led to a relevant, sex-independent increase in Pi10 (univariable linear regression r2 = 0.06 in male participants and 0.09 in female participants), wall thickness (r2 = 0.02 for males and 0.07 for females), and luminal area (r2 = 0.02 for males and 0.01 for females). Smoking status (never, former, and current smokers) also influenced the measured bronchial parameters. The combination of age, sex, height, and weight, as well as smoking status, explained around 46% of variation of the imaging biomarkers.These findings expand on the current evidence regarding CT-based bronchial reference values. Because prior studies derived data from small samples of lung-healthy participants used only as control participants in lung disease cohorts, they were suboptimal in defining normal reference values. The study by Dudurych et al (5) addresses these limitations by including a large lung-healthy population sufficient for establishing normal ranges and normalized reference parameter equations. The authors go beyond the scope of the publication of their research work by making their reference bronchial parameter calculator publicly accessible online. Further strengths of this investigation include the rigorous exclusion of functional or anatomic lung disease, the inclusion of a large-scale balanced population, and the use of a fully-automated artificial intelligence–based method to quantify the bronchial parameters.The normal values proposed by Dudurych et al (5) are different from those previously published by others. This could be explained by several factors, including different methods of calculation, the used scale, and cohort characteristics. However, to our knowledge, their study is the largest systematic evaluation of factors influencing quantitative CT-derived bronchial parameters in a lung-healthy population. Major findings, including that age affects the airways, are in line with prior reports from histologic and micro-CT studies of donor lungs, which demonstrated the loss of elasticity and reduction of density in the aging lung (6,7). Similar results were presented in a recent study (3) of never-smoker male participants, although in a small sample size. Interestingly, that study could not demonstrate such findings in never-smoker female participants.Despite the encouraging results, the study (5) had some limitations. The study included mostly White participants from northern Netherlands. Therefore, the transferability of findings to other ethnicities and body habitus remains unknown. Furthermore, it remains unclear if and how CT scanning protocols, the type of CT scanner systems, and settings affect these imaging biomarkers.Independently of these limitations, the investigation by Dudurych et al (5) is a much-needed step to push bronchial CT parameters toward clinical application. With the expansion of lung cancer screening programs, low-dose chest CT examinations are becoming more widespread. Besides the primary purpose of screening for lung cancer, these CT examinations may offer various additional information about the status of the cardiothoracic health of an individual, which could be used as a clinically relevant opportunistic "bycatch." This information may then trigger lifestyle changes or therapeutic interventions that ultimately could improve patient outcome. An already well-documented example of this is the detection of coronary atherosclerosis at lung cancer screening CT, with documented effects on prognosis and their potential to treat at-risk patients (8). Whereas the quantification and standardization of coronary atherosclerosis measurements from noncontrast-enhanced CT seems simple and has been well-established by several studies (9,10), the derivation of robust imaging biomarkers for bronchial CT parameters appears to be more complicated. Despite the technical challenges in airway segmentation, the generation of well-established reference ranges in the healthy lung is one of the key factors needed to integrate such imaging biomarkers in the clinical workflow. Other than the promise of allowing screening and early-stage identification of pulmonary diseases, CT-derived bronchial parameters could also be used to monitor treatment response after intervention in various pulmonary diseases and could potentially complement existing parameters of lung function testing. Therefore, this study represents a valid and important step in this endeavor. However, there are numerous further steps needed to elucidate the technical robustness, applicability to diverse cohorts of other ethnicities, clinical relevance, and usefulness of CT-derived bronchial parameters.Disclosures of conflicts of interest: T.E. Institutional research support from Siemens Healthineers; consulting fees from Circle Cardiovascular Imaging; speaker fees from Siemens Healthineers; travel support from Siemens Healthineers; advisory board member, Siemens Healthineers; member of the Scientific Committee for the European Society of Cardiovascular Radiology and the Clinical Practice Committee for the Society of Cardiac Magnetic Resonance Imaging. A.V.S. Grant payment to institution from Siemens Healthineers; consulting fees from Elucid Bioimaging; payment for lectures from Siemens Healthineers; support for meetings from Siemens Healthineers; stock/stock options for Elucid Bioimaging.References1. Dudurych I, Garcia-Uceda A, Petersen J, Du Y, Vliegenthart R, de Bruijne M. Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction. Eur Radiol 2023;33(10):6718–6725. MedlineGoogle Scholar2. Kirby M, Smith BM, Tanabe N, et al. Computed tomography total airway count predicts progression to COPD in at-risk smokers. ERJ Open Res 2021;7(4):00307–2021. MedlineGoogle Scholar3. Terada S, Tanabe N, Maetani T, et al. Association of age with computed tomography airway tree morphology in male and female never smokers without lung disease history. Respir Med 2023;214:107278. MedlineGoogle Scholar4. Bhatt SP, Bodduluri S, Nakhmani A, et al. Sex Differences in Airways at Chest CT: Results from the COPDGene Cohort. Radiology 2022;305(3):699–708. Google Scholar5. Dudurych I, Pelgrim GJ, Sidorenkov G, et al. Low-dose CT–derived bronchial parameters in individuals with healthy lungs. Radiology 2024;311(3):e232677. Google Scholar6. Verleden SE, Kirby M, Everaerts S, et al. Small airway loss in the physiologically ageing lung: a cross-sectional study in unused donor lungs. Lancet Respir Med 2021;9(2):167–174. MedlineGoogle Scholar7. Pride NB. Ageing and changes in lung mechanics. Eur Respir J 2005;26(4):563–565. MedlineGoogle Scholar8. Jacobs PC, Gondrie MJA, van der Graaf Y, et al. Coronary artery calcium can predict all-cause mortality and cardiovascular events on low-dose CT screening for lung cancer. AJR Am J Roentgenol 2012;198(3):505–511. MedlineGoogle Scholar9. Sabia F, Balbi M, Ledda RE, et al. Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial. PLoS One 2023;18(5):e0285593. MedlineGoogle Scholar10. Suh YJ, Lee JW, Shin SY, Goo JM, Kim Y, Yong HS. Coronary artery calcium severity grading on non-ECG-gated low-dose chest computed tomography: a multiple-observer study in a nationwide lung cancer screening registry. Eur Radiol 2020;30(7):3684–3691. 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