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The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society
Open accessAbstract
With more than 900 000 confirmed cases worldwide and nearly 50 000 deaths during the first 3 months of 2020, the coronavirus disease 2019 (COVID-19) pandemic has emerged as an unprecedented health care crisis. The spread of COVID-19 has been heterogeneous, resulting in some regions having sporadic transmission and relatively few hospitalized patients with COVID-19 and others having community transmission that has led to overwhelming numbers of severe cases. For these regions, health care delivery has been disrupted and compromised by critical resource constraints in diagnostic testing, hospital beds, ventilators, and health care workers who have fallen ill to the virus exacerbated by shortages of personal protective equipment. Although mild cases mimic common upper respiratory viral infections, respiratory dysfunction becomes the principal source of morbidity and mortality as the disease advances. Thoracic imaging with chest radiography and CT are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pretest probability, risk factors for disease progression, and critical resource constraints. To address this deficit, a multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing patients with COVID-19 across a spectrum of health care environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints. Fourteen key questions, corresponding to 11 decision points within the three scenarios and three additional clinical situations, were rated by the panel based on the anticipated value of the information that thoracic imaging would be expected to provide. The results were aggregated, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of chest radiography and CT in the management of COVID-19.
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“CXR was selected as the imaging input because it is widely available and commonly indicated by guidelines”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [23]
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Current and Emerging Knowledge in COVID-19
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Chamseddine, E., Mansouri, N., Soui, M., & Abed, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109588 · Cited by 112
Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
Schalekamp, S., Huisman, M., van Dijk, R. A., Boomsma, M. F., Jorge, P. F., de Boer, W. S., Herder, G. J. M., Bonarius, M., Groot, O. A., Jong, E., et al. (2020) · Radiology · doi:10.1148/radiol.2020202723 · Cited by 90
Self-citationCOVID-19: A Multimodality Review of Radiologic Techniques, Clinical Utility, and Imaging Features
Manna, S., Wruble, J., Maron, S. Z., Toussie, D., Voutsinas, N., Finkelstein, M. S., Cedillo, M. A., Diamond, J. E., Eber, C. D., Jacobi, A., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200210 · Cited by 91
The Usefulness of Chest CT Imaging in Patients With Suspected or Diagnosed COVID-19
Machnicki, S. C., Patel, D., Singh, A. K., Talwar, A., Mina, B. A., Oks, M., Makkar, P., Naidich, D. P., Mehta, A. C., Hill, N. S., et al. (2021) · CHEST Journal · doi:10.1016/j.chest.2021.04.004 · Cited by 91
Self-citationCT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study
Gietema, H. A., Zelis, N., Nobel, J. M., Lambriks, L., van Alphen, L. B., Lashof, A. M. L. O., Wildberger, J. E., Nelissen, I. C., & Stassen, P. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235844 · Cited by 84
Immediate impact of COVID-19 on transplant activity in the Netherlands
de Vries, A. P. J., Alwayn, I. P. J., Hoek, R. A. S., van den Berg, A. P., Ultee, F. C. W., Vogelaar, S., Haase-Kromwijk, B. J. J. M., Heemskerk, M. B. A., Hemke, A. C., Nijboer, W. N., et al. (2020) · Transplant Immunology · doi:10.1016/j.trim.2020.101304 · Cited by 83
Homayounieh, F., Holmberg, O., Umairi, R. S. A., Aly, S., Basevičius, A., Costa, P. R., Darweesh, A., Gershan, V., Ilves, P., Kostova-Lefterova, D. Z., et al. (2020) · Radiology · doi:10.1148/radiol.2020203453 · Cited by 83
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
Hu, Y., Jacob, J., Parker, G. J. M., Hawkes, D. J., Hurst, J. R., & Stoyanov, D. V. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0185-2 · Cited by 82
Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France
Herpe, G., Léderlin, M., Naudin, M., Ohana, M., Chaumoître, K., Grégory, J., Vilgrain, V., Freitag, C. A., de Margerie‐Mellon, C., Flory, V., et al. (2020) · Radiology · doi:10.1148/radiol.2020202568 · Cited by 77
Falaschi, Z., Danna, P. S. C., Arioli, R., Paschè, A., Zagaria, D., Percivale, I., Tricca, S., Barini, M., Aquilini, F., Andreoni, S., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109192 · Cited by 74
Lázaro-Pérez, C., López, J. Á. M., Galán, J. G., & Menéses, E. L. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17165938 · Cited by 84
CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19
Schiaffino, S., Albano, D., Cozzi, A., Messina, C., Arioli, R., Bnà, C., Bruno, A., Carbonaro, L. A., Carriero, A., Carriero, S., et al. (2021) · Radiology · doi:10.1148/radiol.2021204141 · Cited by 76
Awasthi, N., Dayal, A., Cenkeramaddi, L. R., & Yalavarthy, P. K. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3068190 · Cited by 76
Thoracic imaging tests for the diagnosis of COVID-19
Ebrahimzadeh, S., Islam, N., Dawit, H., Salameh, J., Kazi, S., Fabiano, N., Treanor, L. M., Absi, M., Ahmad, F., Rooprai, P., et al. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013639.pub5 · Cited by 83
Radiological approach to COVID-19 pneumonia with an emphasis on chest CT
Güneyli, S., Atçeken, Z., Doğan, H., Altınmakas, E., & Atasoy, K. C. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.20260 · Cited by 76
Allenbach, Y., Saadoun, D., Maalouf, G., Vieira, M., Hellio, A., Boddaert, J., Gros, H. L., Salem, J., Resche‐Rigon, M., Cherifa, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240711 · Cited by 79
Which role for chest x-ray score in predicting the outcome in COVID-19 pneumonia?
Maroldi, R., Rondi, P., Agazzi, G. M., Ravanelli, M., Borghesi, A., & Farina, D. (2020) · European Radiology · doi:10.1007/s00330-020-07504-2 · Cited by 75
Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation
Lyu, F., Ye, M., Carlsen, J. F., Erleben, K., Darkner, S., & Yuen, P. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3217501 · Cited by 75
Altmayer, S., Zanon, M., Pacini, G. S., Watte, G., Barros, M. C., Mohammed, T.-L. H., Verma, N., Marchiori, E. D. S., & a, B. H. (2020) · European Radiology · doi:10.1007/s00330-020-07018-x · Cited by 73
ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19
Chowdhury, N. K., Kabir, M. A., Rahman, M. M., & Rezoana, N. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.551 · Cited by 86
Imaging of COVID-19: CT, MRI, and PET
Fields, B. K. K., Demirjian, N. L., Dadgar, H., & Gholamrezanezhad, A. (2020) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2020.11.003 · Cited by 72
Coronavirus Disease 2019 in Children
Borrelli, M., Corcione, A., Castellano, F., Nastro, F. F., & Santamaria, F. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.668484 · Cited by 75
Chest CT features of the novel coronavirus disease (COVID-19)
Ufuk, F., & Savaş, R. (2020) · TURKISH JOURNAL OF MEDICAL SCIENCES · doi:10.3906/sag-2004-331 · Cited by 72
Das, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11787-y · Cited by 68
On the role of artificial intelligence in medical imaging of COVID-19
Born, J., Beymer, D., Rajan, D., Coy, A., Mukherjee, V. V., Manica, M., Prasanna, P. M., Ballah, D., Guindy, M., Shaham, D., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100269 · Cited by 73
Orlandi, M., Landini, N., Sambataro, G., Nardi, C., Tofani, L., Bruni, C., Bellando-Randone, S., Blagojević, J., Melchiorre, D., Hughes, M., et al. (2021) · Rheumatology · doi:10.1093/rheumatology/keab615 · Cited by 69
Pediatric lung imaging features of COVID‐19: A systematic review and meta‐analysis
Nino, G., Zember, J., Sanchez‐Jacob, R., Gutierrez, M. J., Sharma, K., & Linguraru, M. G. (2021) · Pediatric Pulmonology · doi:10.1002/ppul.25070 · Cited by 65
An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19
Ching, S. L. W., Chuah, J. H., Tee, C. A. T., Anis, S., Shoaib, M. A., Amir, F., Khalil, A., & Lai, K. W. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5528144 · Cited by 69
Metadata
- Authors
- Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J.-J., Martin, I. B. K., Anderson, D. J., Kong, C., Altes, T., Bush, A., Desai, S. R., Goldin, O., Goo, J. M., Humbert, M., Inoue, Y., Kauczor, H.-U., Luo, F., Mazzone, P. J., Prokop, M., Remy-Jardin, M., Richeldi, L., Schaefer-Prokop, C. M., Tomiyama, N., Wells, A. U., & Leung, A. N.
- Affiliations
- University of Parma (IT)
- Università Cattolica del Sacro Cuore (IT)
- Corresponding author
- Rubin, Geoffrey D.
- ORCID
- Rubin, Geoffrey D.
- Ryerson, Christopher J.
- Haramati, Linda B.
- Sverzellati, Nicola
- Kanne, Jeffrey P.
- Schluger, Neil W.
- Volpi, Annalisa
- Yim, Jae-Joon
- Anderson, Deverick J.
- Kong, Christina
- Altes, Talissa
- Bush, Andrew
- Desai, Sujal R.
- Goo, Jin Mo
- Humbert, Marc
- Inoue, Yoshikazu
- Kauczor, Hans-Ulrich
- Luo, Fengming
- Mazzone, Peter J.
- Prokop, Mathias
- Richeldi, Luca
- Tomiyama, Noriyuki
- Wells, Athol U.
- Leung, Ann N.
- Venue
- Radiology
- Published
- 2020-07
- Volume
- 296
- Issue
- 1
- Pages
- 172-180
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7233395
- OpenAlex
- W4230649743
- Cited by
- 1,101
- References
- 25
- Citations by year
- 2026: 17 · 2025: 35 · 2024: 76 · 2023: 123 · 2022: 189 · 2021: 330 · 2020: 328
- FWCI
- 31.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Keywords
- COVID-19 pandemic, chest imaging, chest radiography, computed tomography, clinical management, diagnostic decision-making, clinical guidelines, community transmission, disease severity, risk factors for progression
- MeSH terms
- Personal Protective Equipment, Betacoronavirus, COVID-19, SARS-CoV-2, Humans, Pneumonia, Viral, Severity of Illness Index, Societies, Medical, Radiography, Thoracic, Triage, Video Recording, Global Health, Coronavirus Infections, Disease Progression, Guideline Adherence, Consensus, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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