{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T02:56:56Z","timestamp":1793933816935,"version":"4.8.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100017246","name":"Radboud Institute for Health Sciences","doi-asserted-by":"publisher","award":["RIHS JO 18.51773"],"award-info":[{"award-number":["RIHS JO 18.51773"]}],"id":[{"id":"10.13039\/501100017246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Objectives<\/jats:title>\n            <jats:p>Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest\u2013abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules. Primary cancers were verified through bronchoscopy with biopsy or cytologic testing. The malignancy status of other nodules was confirmed with bronchoscopy only or follow-up CT scans if such evidence was unavailable. An AI system was trained and evaluated with a ten-fold cross-validation procedure. The performance of the system was assessed with a free-response receiver operating characteristic curve.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We identified 160 patients with airway nodules (median age of 64 years [IQR: 54\u201370], 58 women) and added a random sample of 160 patients without airway nodules (median age of 60 years [IQR: 48\u201369], 80 women). The sensitivity of the AI system was 75.1% (95% CI: 67.6\u201381.6%) for detecting all nodules with an average number of false positives per scan of 0.25 in negative patients and 0.56 in positive patients. At the same operating point, the sensitivity was 79.0% (95% CI: 70.4\u201386.6%) for the subset of tumors. A subgroup analysis showed that the system detected the majority of subtle tumors.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The AI system detects most airway nodules on chest CT with an acceptable false positive rate.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Question<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>Incidental airway tumors are rare and are susceptible to being overlooked on chest CT<\/jats:italic>.<\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Findings<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>An AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features<\/jats:italic>.<\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Clinical relevance<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>An AI system shows potential for supporting radiologists in detecting airway tumors<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00330-025-11468-6","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T08:32:22Z","timestamp":1741163542000},"page":"5615-5625","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence for the detection of airway nodules in chest CT scans"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3357-2959","authenticated-orcid":false,"given":"Ward","family":"Hendrix","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nils","family":"Hendrix","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernst T.","family":"Scholten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bram","family":"van Ginneken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Prokop","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Rutten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"11468_CR1","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1245\/s10434-006-9229-z","volume":"14","author":"J Honings","year":"2007","unstructured":"Honings J, van Dijck JAAM, Verhagen AFTM, van der Heijden HFM, Marres HAM (2007) Incidence and treatment of tracheal cancer: a nationwide study in the Netherlands. Ann Surg Oncol 14:968\u2013976. https:\/\/doi.org\/10.1245\/s10434-006-9229-z","journal-title":"Ann Surg Oncol"},{"key":"11468_CR2","doi-asserted-by":"publisher","first-page":"83","DOI":"10.2147\/OTT.S142847","volume":"11","author":"K Ma","year":"2017","unstructured":"Ma K, Sun F, Yang X et al (2017) Prognosis of patients with primary malignant main stem bronchial tumors: 7,418 cases based on the SEER database. Onco Targets Ther 11:83\u201395. https:\/\/doi.org\/10.2147\/OTT.S142847","journal-title":"OTT"},{"key":"11468_CR3","doi-asserted-by":"publisher","first-page":"3401","DOI":"10.21037\/JTD.2016.11.24","volume":"8","author":"R Stevic","year":"2016","unstructured":"Stevic R, Milenkovic B (2016) Tracheobronchial tumors. J Thorac Dis 8:3401\u20133413. https:\/\/doi.org\/10.21037\/JTD.2016.11.24","journal-title":"J Thorac Dis"},{"key":"11468_CR4","doi-asserted-by":"publisher","unstructured":"Girvin F, Phan A, Steinberger S et al (2023) Malignant and benign tracheobronchial neoplasms: comprehensive review with radiologic, bronchoscopic, and pathologic correlation. Radiographics https:\/\/doi.org\/10.1148\/RG.230045","DOI":"10.1148\/RG.230045"},{"key":"11468_CR5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.21037\/acs.2018.03.09","volume":"7","author":"J-AO Shepard","year":"2018","unstructured":"Shepard J-AO, Flores EJ, Abbott GF (2018) Imaging of the trachea. Ann Cardiothorac Surg 7:197\u2013209. https:\/\/doi.org\/10.21037\/acs.2018.03.09","journal-title":"Ann Cardiothorac Surg"},{"key":"11468_CR6","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.chest.2022.10.039","volume":"163","author":"WB Gefter","year":"2023","unstructured":"Gefter WB, Post BA, Hatabu H (2023) Commonly missed findings on chest radiographs: causes and consequences. Chest 163:650\u2013661. https:\/\/doi.org\/10.1016\/j.chest.2022.10.039","journal-title":"Chest"},{"key":"11468_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/CAAC.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A(2022) Cancer statistics, 2022. CA Cancer J Clin 72:7\u201333. https:\/\/doi.org\/10.3322\/CAAC.21708","journal-title":"CA Cancer J Clin"},{"key":"11468_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S1092-9134(97)80007-X","volume":"1","author":"RW Wilson","year":"1997","unstructured":"Wilson RW, Kirejczyk W (1997) Pathological and radiological correlation of endobronchial neoplasms: part I, benign tumors. Ann Diagn Pathol 1:31\u201346. https:\/\/doi.org\/10.1016\/S1092-9134(97)80007-X","journal-title":"Ann Diagn Pathol"},{"key":"11468_CR9","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.2214\/AJR.04.1893","volume":"186","author":"JM Ko","year":"2006","unstructured":"Ko JM, Jung JI, Park SH et al (2006) Benign tumors of the tracheobronchial tree: CT-pathologic correlation. AJR Am J Roentgenol 186:1304\u20131313. https:\/\/doi.org\/10.2214\/AJR.04.1893","journal-title":"AJR Am J Roentgenol"},{"key":"11468_CR10","doi-asserted-by":"publisher","first-page":"533","DOI":"10.2214\/AJR.12.9354","volume":"201","author":"CC Wu","year":"2013","unstructured":"Wu CC, Khorashadi L, Abbott GF, Gilman MD (2013) Common blind spots on chest CT: Where are they all hiding? Part 1\u2014airways, lungs, and pleura. AJR Am J Roentgenol 201:533\u2013538. https:\/\/doi.org\/10.2214\/AJR.12.9354","journal-title":"AJR Am J Roentgenol"},{"key":"11468_CR11","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1148\/radiology.199.1.8633131","volume":"199","author":"CS White","year":"1996","unstructured":"White CS, Romney BM, Mason AC, Austin JH, Miller BH, Protopapas Z (1996) Primary carcinoma of the lung overlooked at CT: analysis of findings in 14 patients. Radiology 199:109\u2013115. https:\/\/doi.org\/10.1148\/radiology.199.1.8633131","journal-title":"Radiology"},{"key":"11468_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s00330-014-3394-4","volume":"25","author":"ET Scholten","year":"2015","unstructured":"Scholten ET, Horeweg N, de Koning HJ et al (2015) Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening. Eur Radiol 25:81\u201388. https:\/\/doi.org\/10.1007\/s00330-014-3394-4","journal-title":"Eur Radiol"},{"key":"11468_CR13","doi-asserted-by":"publisher","first-page":"e75","DOI":"10.1016\/j.ejrex.2008.06.004","volume":"68","author":"NA Yaakup","year":"2008","unstructured":"Yaakup NA, Gan GC, Kumar G (2008) Missed endobronchial carcinoid tumor presenting as recurrent pneumonia. Eur J Radiol Extra 68:e75\u2013e78. https:\/\/doi.org\/10.1016\/j.ejrex.2008.06.004","journal-title":"Eur J Radiol Extra"},{"key":"11468_CR14","doi-asserted-by":"publisher","first-page":"104806","DOI":"10.1016\/j.compbiomed.2021.104806","volume":"137","author":"Y Gu","year":"2021","unstructured":"Gu Y, Chi J, Liu J et al (2021) A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput Biol Med 137:104806. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104806","journal-title":"Comput Biol Med"},{"key":"11468_CR15","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1016\/J.CRAD.2021.07.012","volume":"76","author":"CO Martins Jarnalo","year":"2021","unstructured":"Martins Jarnalo CO, Linsen PVM, Blaz\u00eds SP, van der Valk PHM, Dickerscheid DBM (2021) Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital. Clin Radiol 76:838\u2013845. https:\/\/doi.org\/10.1016\/J.CRAD.2021.07.012","journal-title":"Clin Radiol"},{"key":"11468_CR16","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s43856-023-00388-5","volume":"3","author":"W Hendrix","year":"2023","unstructured":"Hendrix W, Hendrix N, Scholten ET et al (2023) Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. Commun Med 3:156. https:\/\/doi.org\/10.1038\/s43856-023-00388-5","journal-title":"Commun Med"},{"key":"11468_CR17","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.acra.2020.07.030","volume":"29","author":"K Suzuki","year":"2022","unstructured":"Suzuki K, Otsuka Y, Nomura Y, Kumamaru KK, Kuwatsuru R, Aoki S (2022) Development and validation of a modified three-dimensional U-net deep-learning model for automated detection of lung nodules on chest CT images from the Lung Image Database Consortium and Japanese Datasets. Acad Radiol 29:11. https:\/\/doi.org\/10.1016\/j.acra.2020.07.030","journal-title":"Acad Radiol"},{"key":"11468_CR18","doi-asserted-by":"publisher","first-page":"e0266799","DOI":"10.1371\/journal.pone.0266799","volume":"17","author":"JT Murchison","year":"2022","unstructured":"Murchison JT, Ritchie G, Senyszak D et al (2022) Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population. PLoS One 17:e0266799. https:\/\/doi.org\/10.1371\/journal.pone.0266799","journal-title":"PLoS One"},{"key":"11468_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-022-00938-8","volume":"22","author":"S Katase","year":"2022","unstructured":"Katase S, Ichinose A, Hayashi M et al (2022) Development and performance evaluation of a deep learning lung nodule detection system. BMC Medical Imaging 22:203. https:\/\/doi.org\/10.1186\/s12880-022-00938-8","journal-title":"BMC Medical Imaging"},{"key":"11468_CR20","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.jacr.2023.09.009","volume":"21","author":"J Christensen","year":"2024","unstructured":"Christensen J, Prosper AE, Wu CC et al (2024) ACR lung-RADS v2022: assessment categories and management recommendations. J Am Coll Radiol 21:473\u2013488. https:\/\/doi.org\/10.1016\/j.jacr.2023.09.009","journal-title":"J Am Coll Radiol"},{"key":"11468_CR21","doi-asserted-by":"publisher","unstructured":"DeSimone AK, Byrne SC, Hammer MM (2024) Comparison of lung-RADS version 1.1 and lung-RADS version 2022 in classifying airway nodules detected at lung cancer screening CT. Radiol Cardiothorac Imaging. https:\/\/doi.org\/10.1148\/ryct.230149","DOI":"10.1148\/ryct.230149"},{"key":"11468_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"AAA Setio","year":"2017","unstructured":"Setio AAA, Traverso A, de Bel T et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal 42:1\u201313. https:\/\/doi.org\/10.1016\/j.media.2017.06.015","journal-title":"Med Image Anal"},{"key":"11468_CR23","unstructured":"World Health Organization (2013) International classification of diseases for oncology (ICD-O) 3rd edn. World Health Organization, Switzerland"},{"key":"11468_CR24","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1183\/13993003.01992-2015","volume":"47","author":"H-J Kim","year":"2016","unstructured":"Kim H-J, Kim DK, Kim YW et al (2016) Outcome of incidentally detected airway nodules. Eur Respir J 47:1510\u20131517. https:\/\/doi.org\/10.1183\/13993003.01992-2015","journal-title":"Eur Respir J"},{"key":"11468_CR25","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1001\/jama.2023.2065","volume":"329","author":"AA Diaz","year":"2023","unstructured":"Diaz AA, Orejas JL, Grumley S et al (2023) Airway-occluding mucus plugs and mortality in patients with chronic obstructive pulmonary disease. JAMA 329:1832\u20131839. https:\/\/doi.org\/10.1001\/jama.2023.2065","journal-title":"JAMA"},{"key":"11468_CR26","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1164\/rccm.202006-2248OC","volume":"203","author":"EM Dunican","year":"2021","unstructured":"Dunican EM, Elicker BM, Henry T et al (2021) Mucus plugs and emphysema in the pathophysiology of airflow obstruction and hypoxemia in smokers. Am J Respir Crit Care Med 203:957\u2013968. https:\/\/doi.org\/10.1164\/rccm.202006-2248OC","journal-title":"Am J Respir Crit Care Med"},{"key":"11468_CR27","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TMI.2006.871547","volume":"25","author":"J-M Kuhnigk","year":"2006","unstructured":"Kuhnigk J-M, Dicken V, Bornemann L et al (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 25:417\u2013434. https:\/\/doi.org\/10.1109\/TMI.2006.871547","journal-title":"IEEE Trans Med Imaging"},{"key":"11468_CR28","doi-asserted-by":"publisher","unstructured":"Jocher G, Stoken A, Borovec J et al (2021) ultralytics\/yolov5: v5.0\u2014YOLOv5-P6 1280 models, AWS, Supervisely and YouTube integrations. Zenodo https:\/\/doi.org\/10.5281\/zenodo.4679653","DOI":"10.5281\/zenodo.4679653"},{"key":"11468_CR29","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics YOLOv8. Ultralytics. https:\/\/github.com\/ultralytics\/ultralytics. Accessed 23 Nov 2023"},{"key":"11468_CR30","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"11468_CR31","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"11468_CR32","doi-asserted-by":"publisher","first-page":"982","DOI":"10.2214\/AJR.13.11392","volume":"202","author":"SR Hong","year":"2014","unstructured":"Hong SR, Lee YJ, Hong YJ et al (2014) Differentiation between mucus secretion and endoluminal tumors in the airway: analysis and comparison of CT findings. AJR Am J Roentgenol 202:982\u2013988. https:\/\/doi.org\/10.2214\/AJR.13.11392","journal-title":"AJR Am J Roentgenol"},{"key":"11468_CR33","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1148\/rg.2018170107","volume":"38","author":"LP Busby","year":"2018","unstructured":"Busby LP, Courtier JL, Glastonbury CM (2018) Bias in radiology: the how and why of misses and misinterpretations. Radiographics 38:236\u2013247. https:\/\/doi.org\/10.1148\/rg.2018170107","journal-title":"Radiographics"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11468-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11468-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11468-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T15:08:41Z","timestamp":1755097721000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11468-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["11468"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11468-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,5]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Colin Jacobs.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies: B.v.G. is a shareholder and co-founder of Thirona. He reports no other relationships that are related to the subject matter of the article. M.P. receives grants from Canon Medical Systems, Siemens Healthineers; royalties from Mevis Medical Solutions; payment for lectures from Canon Medical Systems, Siemens Healthineers. The host institution of M.P. is a minority shareholder in Thirona. He reports no other relationships that are related to the subject matter of the article. The host institution of C.J. receives research grants and royalties from MeVis Medical Solutions, Bremen, Germany, and payment for lectures from Canon Medical Systems. C.J. is a collaborator in a public-private research project where Radboudumc collaborates with Philips Medical Systems (Best, the Netherlands). C.J. is a member of the Scientific Editorial Board for European Radiology (section: Imaging Informatics and Artificial Intelligence). He has not taken part in the selection or review processes for this article. He reports no other relationships that are related to the subject matter of the article. The remaining authors declare no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}