Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

Colombi, Davide; Bodini, Flavio C.; Petrini, Marcello; Maffi, Gabriele; Morelli, Nicola; Milanese, Gianluca; Silva, Mario; Sverzellati, Nicola; Michieletti, Emanuele

doi:10.1148/radiol.2020201433

Cited by 457COVID-19 Clinical Research StudiesCOVID-19 diagnosis using AILong-Term Effects of COVID-19

Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

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Colombi, Davide, Bodini, Flavio C., Petrini, Marcello, Maffi, Gabriele, Morelli, Nicola, Milanese, Gianluca, Silva, Mario, Sverzellati, Nicola +1 more·2020·Radiology

DOI 10.1148/radiol.2020201433PMC PMC7233411

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Abstract

Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Online supplemental material is available for this article.

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The area under the receiver operating characteristic curve (AUC) was computed to determine the adverse effects which support the clinical decision making for appropriate patient care.
“The area under the receiver operating characteristic curve (AUC) was then computed to determine the adverse effects which support the clinical decision making for appropriate patient care.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]
Logistic regression models were used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit admission or death versus no ICU admission or death).
“More specifically, logistic regression models were used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit admission or death versus no ICU admission or death).”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]
ML-based regression models have been used to estimate adverse outcome in COVID-19 pneumonia, by using chest CT scans with reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus.
“For example, ML-based regression models have been used in [22] to estimate adverse outcome in COVID-19 pneumonia, by using chest CT scans with reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [22]

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Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884 · see citing works
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · see citing works
Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
Epidemiological and clinical characteristics of 99 cases of 2019 novel Coronavirus pneumonia in Wuhan, China: a descriptive study.
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415 · see citing works
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575 · see citing works
3D Slicer as an image computing platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095 · see citing works
Lung recruitment in patients with the acute respiratory distress syndrome.
Gattinoni, L., Caironi, P., Cressoni, M., Chiumello, D., Ranieri, V. M., Quintel, M., Russo, S., Patroniti, N., Cornejo, R., & Bugedo, G. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa052052 · Cited by 1,548 · see citing works
The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic
Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J., Martin, I. B. K., et al. (2020) · CHEST Journal · doi:10.1016/j.chest.2020.04.003 · Cited by 1,249 · see citing works
Self-citation
The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society
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., et al. (2020) · Radiology · doi:10.1148/radiol.2020201365 · Cited by 1,101 · see citing works
Self-citation
Pressure-Volume Curve of Total Respiratory System in Acute Respiratory Failure: Computed Tomographic Scan Study
Gattinoni, L., Pesenti, A. M., Avalli, L., Rossi, F., & Bombino, M. (1987) · American Review of Respiratory Disease · doi:10.1164/ajrccm/136.3.730 · Cited by 892 · see citing works
The concept of “baby lung”
Gattinoni, L., & Pesenti, A. M. (2005) · Intensive Care Medicine · doi:10.1007/s00134-005-2627-z · Cited by 837 · see citing works
Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - Secondary Publication
Simpson, S. A., Kay, F. U., Abbara, S., Bhalla, S., Chung, J. H., Chung, M., Henry, T. S., Kanne, J. P., Kligerman, S. J., Ko, J. P., et al. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000524 · Cited by 774 · see citing works
Radiological Society of North America Expert Consensus Document on Reporting Chest CT Findings Related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA
Simpson, S. A., Kay, F. U., Abbara, S., Bhalla, S., Chung, J. H., Chung, M., Henry, T. S., Kanne, J. P., Kligerman, S. J., Ko, J. P., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200152 · Cited by 764 · see citing works
CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)
Li, K., Fang, Y., Li, W., Pan, C., Qin, P., Zhong, Y., Liu, X., Huang, M., Liao, Y., & Li, S. (2020) · European Radiology · doi:10.1007/s00330-020-06817-6 · Cited by 721 · see citing works
Obesity and its implications for covid-19 mortality
Dietz, W., & Santos‐Burgoa, C. (2020) · Obesity · doi:10.1002/oby.22818 · Cited by 687 · see citing works
Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19
Yang, R., Li, X., Liu, H., Zhen, Y., Zhang, X., Xiong, Q., Luo, Y., Gao, C., & Zeng, W. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200047 · Cited by 684 · see citing works
Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach
Huang, L., Han, R., Ai, T., Yu, P., Kang, H. S., Tao, Q., & Xia, L. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200075 · Cited by 424 · see citing works
Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score
Guo, L., Wei, D., Zhang, X., Wu, Y., Li, Q. Y., Zhou, M., & Qu, J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02752 · Cited by 412 · see citing works
Idiopathic Pulmonary Fibrosis: Physiologic Tests, Quantitative CT Indexes, and CT Visual Scores as Predictors of Mortality
Best, A. C., Meng, J., Lynch, A. M., Bozic, C. M., Miller, D. E., Grunwald, G. K., & Lynch, D. A. (2008) · Radiology · doi:10.1148/radiol.2463062200 · Cited by 322 · see citing works
Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis
Maldonado, F., Moua, T., Rajagopalan, S., Karwoski, R. A., Raghunath, S. M., Decker, P. A., Hartman, T. E., Bartholmai, B. J., Robb, R. A., & Ryu, J. H. (2013) · European Respiratory Journal · doi:10.1183/09031936.00071812 · Cited by 264 · see citing works
CT-quantified emphysema in male heavy smokers: association with lung function decline
Hoesein, F. A. A. M., de Hoop, B., Zanen, P., Gietema, H. A., Kruitwagen, C. L. J. J., van Ginneken, B., Išgum, I., Mol, C. P., van Klaveren, R. J., Dijkstra, A., et al. (2011) · Thorax · doi:10.1136/thx.2010.145995 · Cited by 176 · see citing works
Quantitative CT Analysis of Diffuse Lung Disease
Chen, A., Karwoski, R. A., Gierada, D. S., Bartholmai, B. J., & Koo, C. W. (2019) · Radiographics · doi:10.1148/rg.2020190099 · Cited by 171 · see citing works
Fibrotic idiopathic interstitial pneumonias: HRCT findings that predict mortality
Edey, A., Devaraj, A., Barker, R. P., Nicholson, A. G., Wells, A. U., & Hansell, D. M. (2011) · European Radiology · doi:10.1007/s00330-011-2098-2 · Cited by 144 · see citing works
Fibroproliferative changes on high-resolution CT in the acute respiratory distress syndrome predict mortality and ventilator dependency: a prospective observational cohort study
Ichikado, K., Muranaka, H., Gushima, Y., Kotani, T., Nader, H. M., Fujimoto, K., Johkoh, T., Iwamoto, N., Kawamura, K., Nagano, J., et al. (2012) · BMJ Open · doi:10.1136/bmjopen-2011-000545 · Cited by 132 · see citing works
Automated segmentation of lungs with severe interstitial lung disease in CT
Wang, J., Li, F., & Li, Q. (2009) · Medical Physics · doi:10.1118/1.3222872 · Cited by 125 · see citing works
Effect of Emphysema Extent on Serial Lung Function in Patients with Idiopathic Pulmonary Fibrosis
Cottin, V., Hansell, D. M., Sverzellati, N., Weycker, D. A., Antoniou, K. M., Atwood, M., Oster, G., Kirchgaessler, K.-U., Collard, H. R., & Wells, A. U. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201612-2492oc · Cited by 111 · see citing works
Self-citation
Integrated Radiologic Algorithm for COVID-19 Pandemic
Sverzellati, N., Milanese, G., Milone, F., Balbi, M., Ledda, R. E., & Silva, M. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000516 · Cited by 76 · see citing works
Self-citation
Hospitalized Patients With 2009 H1N1 Influenza Infection: The Mayo Clinic Experience
Venkata, C. V., Sampathkumar, P., & Afessa, B. (2010) · Mayo Clinic Proceedings · doi:10.4065/mcp.2010.0166 · Cited by 66 · see citing works
Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system
Yoon, R. G., Seo, J. B., Kim, N., Lee, H. J., Lee, S. M., Lee, Y. K., Song, J., Song, J. W., & Kim, D. S. (2012) · European Radiology · doi:10.1007/s00330-012-2634-8 · Cited by 55 · see citing works
A predictive factor for patients with acute respiratory distress syndrome: CT lung volumetry of the well-aerated region as an automated method
Nishiyama, A., Kawata, N., Yokota, H., Sugiura, T., Matsumura, Y., Higashide, T., Horikoshi, T., Oda, S., Tatsumi, K., & Uno, T. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108748 · Cited by 49 · see citing works
Quantitative CT Evaluation in Patients with Combined Pulmonary Fibrosis and Emphysema
Matsuoka, S., Yamashiro, T., Matsushita, S., Kotoku, A., Fujikawa, A., Yagihashi, K., & Nakajima, Y. (2015) · Academic Radiology · doi:10.1016/j.acra.2015.01.008 · Cited by 45 · see citing works
Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates
Tong, Y., Udupa, J. K., Torigian, D. A., Odhner, D., Wu, C., Pednekar, G. V., Palmer, S. M., Rozenshtein, A., Shirk, M. A., Newell, J. D., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168932 · Cited by 45 · see citing works
Structured reporting for fibrosing lung disease: a model shared by radiologist and pulmonologist
Consort, T. I. S. R. O. F. L. D., Sverzellati, N., Odone, A., Silva, M., Polverosi, R., Florio, C., Cardinale, L., Cortese, G., Addonisio, G., Zompatori, M., et al. (2017) · La radiologia medica · doi:10.1007/s11547-017-0835-6 · Cited by 34 · see citing works
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Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070 · see citing works
Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients
Dessie, Z. G., & Zewotir, T. T. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-06536-3 · Cited by 875 · see citing works
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Francone, M., Iafrate, F., Masci, G. M., Coco, S., Cilia, F., Manganaro, L., Panebianco, V., Andreoli, C., Colaiacomo, M. C., Zingaropoli, M. A., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07033-y · Cited by 806 · see citing works
Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review
Izcovich, A., Ragusa, M. A., Tortosa, F. G., Marzio, M. A. L., Agnoletti, C., Bengolea, A., Ceirano, A., Espinosa, F., Saavedra, E., Sanguine, V., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241955 · Cited by 658 · see citing works
Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies
Parohan, M., Yaghoubi, S., Seraji, A., Javanbakht, M. H., Sarraf, P., & Djalali, M. (2020) · The Aging Male · doi:10.1080/13685538.2020.1774748 · Cited by 469 · see citing works
Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: a meta-analysis
de Almeida-Pititto, B., Dualib, P. M., Zajdenverg, L., Dantas, J. R., de Souza, F. D., Rodacki, M., Bertoluci, M. C., & Brazilian Diabetes Society Study Group (SBD) (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00586-4 · Cited by 391 · see citing works
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286 · see citing works
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Poggiali, E., Zaino, D., Immovilli, P., Rovero, L., Losi, G., Dacrema, A., Nuccetelli, M., Vadacca, G. B., Guidetti, D., Vercelli, A., et al. (2020) · Clinica Chimica Acta · doi:10.1016/j.cca.2020.06.012 · Cited by 272 · see citing works
Deconditioning as main mechanism of impaired exercise response in COVID-19 survivors
Rinaldo, R. F., Mondoni, M., Parazzini, E. M., Pitari, F., Brambilla, E., Luraschi, S., Balbi, M., Papa, G. F. S., Sotgiu, G., Guazzi, M., et al. (2021) · European Respiratory Journal · doi:10.1183/13993003.00870-2021 · Cited by 230 · see citing works
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Gupta, R. K., Marks, M., Samuels, T. H. A., Luintel, A., Rampling, T., Chowdhury, H., Quartagno, M., Nair, A., Lipman, M. C. I., Abubakar, I., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.03498-2020 · Cited by 189 · see citing works
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Islam, N., Ebrahimzadeh, S., Salameh, J., Kazi, S., Fabiano, N., Treanor, L. M., Absi, M., Hallgrimson, Z., Leeflang, M., Hooft, L., et al. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013639.pub4 · Cited by 199 · see citing works
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Current smoking, former smoking, and adverse outcome among hospitalized COVID-19 patients: a systematic review and meta-analysis
Farsalinos, K. E., Barbouni, A., Poulas, K. A., Polosa, R., Caponnetto, P., & NIAURA, R. S. (2020) · Therapeutic Advances in Chronic Disease · doi:10.1177/2040622320935765 · Cited by 171 · see citing works
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation
Lanza, E., Muglia, R., Bolengo, I., Santonocito, O. G., Lisi, C., Angelotti, G., Morandini, P., Savevski, V., Politi, L. S., & Balzarini, L. (2020) · European Radiology · doi:10.1007/s00330-020-07013-2 · Cited by 149 · see citing works
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Estiri, H., Strasser, Z. H., Klann, J. G., Naseri, P., Wagholikar, K. B., & Murphy, S. N. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00383-x · Cited by 142 · see citing works
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Nouvenne, A., Zani, M. D., Milanese, G., Parise, A., Baciarello, M., Bignami, E. G., Odone, A., Sverzellati, N., Meschi, T., & Ticinesi, A. (2020) · Respiration · doi:10.1159/000509223 · Cited by 118 · see citing works
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Huang, C., Soleimani, J., Herasevich, S., Pinevich, Y., Pennington, K. M., Dong, Y., Pickering, B. W., & Barwise, A. K. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.10.022 · Cited by 90 · see citing works
CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification
Goncharov, M., Pisov, M., Shevtsov, A. E., Shirokikh, B., Kurmukov, A. G., Blokhin, I. A., Chernina, V., Solovev, A. V., Gombolevskiy, V. А., Morozov, S. P., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102054 · Cited by 87 · see citing works
Treatment of COVID-19 With Conestat Alfa, a Regulator of the Complement, Contact Activation and Kallikrein-Kinin System
Urwyler, P., Moser, S., Charitos, P., Heijnen, I., Rudin, M., Sommer, G., Giannetti, B., Bassetti, S., Sendi, P., Trendelenburg, M., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.02072 · Cited by 84 · see citing works
Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments
Casiraghi, E., Malchiodi, D., Trucco, G., Frasca, M., Cappelletti, L., Fontana, T., Esposito, A. A., Avola, E., Jachetti, A., Reese, J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3034032 · Cited by 79 · see citing works
Thoracic imaging tests for the diagnosis of COVID-19
Salameh, J., Leeflang, M., Hooft, L., Islam, N., McGrath, T. A., van der Pol, C. B., Frank, R. A., Prager, R. T., Hare, S. S., Dennie, C. J., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013639.pub2 · Cited by 79 · see citing works
Cytokine Release Syndrome (CRS) and Nicotine in COVID-19 Patients: Trying to Calm the Storm
González-Rubio, J., Navarro-López, C., López-Nájera, E., López-Nájera, A., Jiménez‐Díaz, L., Navarro‐López, J. D., & López, A. N. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01359 · Cited by 77 · see citing works
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 · see citing works
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 79 · see citing works
Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency
Ferrari, D., Milić, J., Tonelli, R., Ghinelli, F., Meschiari, M., Volpi, S., Faltoni, M., Franceschi, G., Iadisernia, V., Yaacoub, D., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239172 · Cited by 75 · see citing works
Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review
Larici, A. R., Cicchetti, G., Marano, R., Merlino, B., Elia, L., Calandriello, L., del Ciello, A., Farchione, A., Savino, G., Infante, A., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109217 · Cited by 72 · see citing works
Different Methods to Improve the Monitoring of Noninvasive Respiratory Support of Patients with Severe Pneumonia/ARDS Due to COVID-19: An Update
Pelosi, P. P., Tonelli, R., Torregiani, C., Baratella, E., Confalonieri, M., Battaglini, D., Marchioni, A., Confalonieri, P., Clini, E. M., Salton, F., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11061704 · Cited by 75 · see citing works
A Systematic Review and Meta-Analysis of Hospitalised Current Smokers and COVID-19
González-Rubio, J., Navarro-López, C., López-Nájera, E., López-Nájera, A., Jiménez‐Díaz, L., Navarro‐López, J. D., & López, A. N. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17207394 · Cited by 69 · see citing works
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 70 · see citing works
Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis
Bellou, V., Tzoulaki, I., van Smeden, M., Moons, K. G. M., Εvangelou, Ε., & Belbasis, L. (2021) · European Respiratory Journal · doi:10.1183/13993003.02964-2020 · Cited by 68 · see citing works
Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data
Wang, R., Jiao, Z., Yang, L., Choi, J. W., Xiong, Z., Halsey, K., Tran, T. M. L., Pan, I., Collins, S., Feng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08049-8 · Cited by 67 · see citing works
Machine learning for COVID-19—asking the right questions
Bächtiger, P., Peters, N. S., & Walsh, S. L. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30162-x · Cited by 66 · see citing works
Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis
Cappabianca, S., Fusco, R., Lisio, A. D., Paura, C., Clemente, A., Gagliardi, G., Lombardi, G., Giacobbe, G., Maria, R. G., Paola, B. M., et al. (2020) · La radiologia medica · doi:10.1007/s11547-020-01293-w · Cited by 63 · see citing works
Imaging findings in COVID-19 pneumonia
de Farias, L. D. P. G., Fonseca, E. K. U. N., Strabelli, D. G., Loureiro, B. M. C., Neves, Y. C. S., Rodrigues, T. P., Chate, R. C., Nomura, C. H., Sawamura, M. V. Y., & Cerri, G. G. (2020) · Clinics · doi:10.6061/clinics/2020/e2027 · Cited by 61 · see citing works
The incidence of pleural effusion in COVID-19 pneumonia: State-of-the-art review
Chong, W. H., Saha, B. K., Conuel, E. J., & Chopra, A. (2021) · Heart & Lung · doi:10.1016/j.hrtlng.2021.02.015 · Cited by 61 · see citing works
Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19
Grodecki, K., Lin, A., Razipour, A., Cadet, S. J., McElhinney, P. A., Chan, C., Pressman, B. D., Julien, P. J., Maurovich‐Horvat, P., Gaibazzi, N., et al. (2020) · Metabolism · doi:10.1016/j.metabol.2020.154436 · Cited by 60 · see citing works
Chest CT–derived pulmonary artery enlargement at the admission predicts overall survival in COVID-19 patients: insight from 1461 consecutive patients in Italy
Esposito, A., Palmisano, A., Toselli, M., Vignale, D., Cereda, A. F., Rancoita, P. M. V., Leone, R., Nicoletti, V., Gnasso, C., Monello, A., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07622-x · Cited by 60 · see citing works
Imaging in corona virus disease 2019 (COVID-19)—A Scoping review
Jajodia, A., Ebner, L., Heidinger, B. H., Chaturvedi, A., & Prosch, H. (2020) · European Journal of Radiology Open · doi:10.1016/j.ejro.2020.100237 · Cited by 58 · see citing works
An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study
Kim, H., Han, D., Kim, J.-H., Kim, D., Ha, B., Seog, W., Lee, Y.-K., Lim, D., Hong, S. O., Park, M.-J., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/24225 · Cited by 58 · see citing works
Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method
Leonardi, A., Scipione, R., Alfieri, G., Petrillo, R., Dolciami, M., Ciccarelli, F., Perotti, S., Cartocci, G., Scala, A., Imperiale, C., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109202 · Cited by 57 · see citing works
Comparison of admission chest computed tomography and lung ultrasound performance for diagnosis of COVID-19 pneumonia in populations with different disease prevalence
Colombi, D., Petrini, M., Maffi, G., Villani, G. D., Bodini, F. C., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109344 · Cited by 57 · see citing works
Self-citation
Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course: design of the Maastricht Intensive Care COVID cohort (MaastrICCht)
Maastricht Intensive Care COVID Study Group; MaastrICCht Collaborators, Tas, J., van Gassel, R. J. J., Heines, S. J., Mulder, M. M. G., Heijnen, N. F. L., de Jong, M. J., Bels, J. L. M., Bennis, F. C., Koelmann, M., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-040175 · Cited by 56 · see citing works
COVID-19 pneumonia: Diagnostic and prognostic role of CT based on a retrospective analysis of 214 consecutive patients from Paris, France
Guillo, E., Gómez, I. B., Dangeard, S., Bennani, S., Saab, I., Tordjman, M., Jilet, L., Chassagnon, G., & Revel, M. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109209 · Cited by 55 · see citing works
CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia
Homayounieh, F., Ebrahimian, S., Babaei, R., Mobin, H. K., Zhang, E. W., Bizzo, B. C., Mohseni, I., Digumarthy, S. R., & Kalra, M. K. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200322 · Cited by 54 · see citing works
Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome
de Matos, J. F. R. G., Paparo, F., Mussetto, I., Bacigalupo, L., Veneziano, A., Bernardi, S., Biscaldi, E., Melani, E. F., Antonucci, G., Cremonesi, P., et al. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-00167-0 · Cited by 52 · see citing works
COVID-19: a brief update for radiologists
Meirelles, G. D. S. P. (2020) · Radiologia Brasileira · doi:10.1590/0100-3984.2020.0074 · Cited by 52 · see citing works
Clinical and chest computed tomography features of patients suffering from mild and severe COVID-19 at Fayoum University Hospital in Egypt
Ismail, A., Doghish, A. S., Elkhatib, W. F., Magdy, A., Mahmoud, E. E., Ahmed, M. I., & Khalil, M. A. F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271271 · Cited by 52 · see citing works
Predictors of in-hospital mortality AND death RISK STRATIFICATION among COVID-19 PATIENTS aged ≥ 80 YEARs OLD
Covino, M., Matteis, G. D., Polla, D. A. D., Santoro, M. C., Burzo, M. L., Torelli, E., Simeoni, B., Russo, A., Sandroni, C., Gasbarrini, A., et al. (2021) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2021.104383 · Cited by 51 · see citing works
Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software
Grassi, R., Cappabianca, S., Urraro, F., Feragalli, B., Montanelli, A., Patelli, G., Granata, V., Giacobbe, G., Russo, G. M., Grillo, A., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186914 · Cited by 50 · see citing works
Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients
Zarifian, A., Nour, M. G., Rezayat, A. A., Oskooei, R. R., Abbasi, B., & Sadeghi, R. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.10.035 · Cited by 48 · see citing works
Lung ultrasound predicts non-invasive ventilation outcome in COVID-19 acute respiratory failure: a pilot study
Biasucci, D. G., on behalf of the Gemelli Against COVID-19 Group, Buonsenso, D., PIANO, A., Bonadia, N., Vargas, J., Settanni, D., Bocci, M. G., Grieco, D. L., Carnicelli, A., et al. (2021) · Minerva Anestesiologica · doi:10.23736/s0375-9393.21.15188-0 · Cited by 46 · see citing works
Fatigue Symptoms Associated With COVID-19 in Convalescent or Recovered COVID-19 Patients; a Systematic Review and Meta-Analysis
Rao, S., Benzouak, T., Gunpat, S., Burns, R. J., Tahir, T. A., Jolles, S. R. A., & Kisely, S. R. (2021) · Annals of Behavioral Medicine · doi:10.1093/abm/kaab081 · Cited by 47 · see citing works
Clinical utility of chest radiography for severe COVID-19
Hui, T. C. H., Khoo, H. W., Young, B. E., Mohideen, S. M. H., Lee, Y. S., Lim, C. J., Leo, Y., Kaw, G. J. L., Lye, D. C., & Tan, C. H. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-642 · Cited by 45 · see citing works
The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19
Eslami, V., Abrishami, A., Zarei, E., Khalili, N., Baharvand, Z., & Sanei‐Taheri, M. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.09.012 · Cited by 44 · see citing works
Impact of coronavirus disease 2019 (COVID-19) emergency on Italian radiologists: a national survey
Albano, D., Bruno, A., Bruno, F., Calandri, M., Caruso, D., Clemente, A., Coppolino, P., Cozzi, D., Robertis, R. D., Gentili, F., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07046-7 · Cited by 43 · see citing works
Deep neural networks ensemble to detect COVID-19 from CT scans
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108135 · Cited by 44 · see citing works
Predictive value of CT in the short-term mortality of Coronavirus Disease 2019 (COVID-19) pneumonia in nonelderly patients: A case-control study
Tabatabaei, S. M. H., Rahimi, H., Moghaddas, F., & Rajebi, H. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109298 · Cited by 43 · see citing works
Coronary and total thoracic calcium scores predict mortality and provides pathophysiologic insights in COVID-19 patients
Giannini, F., Toselli, M., Palmisano, A., Cereda, A. F., Vignale, D., Leone, R., Nicoletti, V., Gnasso, C., Monello, A., Manfrini, M., et al. (2021) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2021.03.003 · Cited by 43 · see citing works
Self-citation
Medical imaging and computational image analysis in COVID-19 diagnosis: A review
Nabavi, S., Ejmalian, A., Moghaddam, M. E., Abin, A. A., Frangi, A. F., Mohammadi, M. H., & Rad, H. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104605 · Cited by 43 · see citing works
Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients
Colombi, D., Villani, G. D., Maffi, G., Risoli, C., Bodini, F. C., Petrini, M., Morelli, N., Anselmi, P., Milanese, G., Silva, M., et al. (2020) · Emergency Radiology · doi:10.1007/s10140-020-01867-1 · Cited by 42 · see citing works
Self-citation
Severity does not impact on exercise capacity in COVID-19 survivors
Rinaldo, R. F., Mondoni, M., Parazzini, E. M., Baccelli, A., Pitari, F., Brambilla, E., Luraschi, S., Balbi, M., Guazzi, M., Marco, F. D., et al. (2021) · Respiratory Medicine · doi:10.1016/j.rmed.2021.106577 · Cited by 42 · see citing works
The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)
Inui, S., Gonoi, W., Kurokawa, R., Nakai, Y., Watanabe, Y., Sakurai, K., Ishida, M., Fujikawa, A., & Abe, O. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01096-1 · Cited by 42 · see citing works
Severity of lung involvement on chest X-rays in SARS-coronavirus-2 infected patients as a possible tool to predict clinical progression: an observational retrospective analysis of the relationship between radiological, clinical, and laboratory data
Baratella, E., Crivelli, P., Marrocchio, C., Bozzato, A. M., VITO, A. D., Madeddu, G., Saderi, L., Confalonieri, M., Tenaglia, L., & Cova, M. A. (2020) · Jornal Brasileiro de Pneumologia · doi:10.36416/1806-3756/e20200226 · Cited by 42 · see citing works
Recommendations of the Thoracic Imaging Section of the German Radiological Society for clinical application of chest imaging and structured CT reporting in the COVID-19 pandemic
Vogel‐Claussen, J., Ley‐Zaporozhan, J., Agarwal, P., Biederer, J., Kauczor, H., Ley, S., Kühl, H., Müeller-Lisse, U. G., ten Persigehl, T., Schlett, C. L., et al. (2020) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-1174-8378 · Cited by 41 · see citing works
Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal
Neri, E., Coppola, F., Larici, A. R., Sverzellati, N., Mazzei, M. A., Sacco, P., Dalpiaz, G., Feragalli, B., Miele, V., & Grassi, R. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00901-7 · Cited by 41 · see citing works
Self-citation
Magnetic resonance imaging features of coronavirus disease 2019 (COVID-19) pneumonia: The first preliminary case series
Torkian, P., Rajebi, H., Zamani, T., ramezani, N., Kiani, P., & Akhlaghpoor, S. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.09.002 · Cited by 41 · see citing works
Quantitative Burden of COVID-19 Pneumonia at Chest CT Predicts Adverse Outcomes: A Post Hoc Analysis of a Prospective International Registry
Grodecki, K., Lin, A., Cadet, S. J., McElhinney, P. A., Razipour, A., Chan, C., Pressman, B. D., Julien, P. J., Maurovich‐Horvat, P., Gaibazzi, N., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200389 · Cited by 40 · see citing works
Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model
Leoni, M. L. G., Lombardelli, L., Colombi, D., Bignami, E. G., Pergolotti, B., Repetti, F., Villani, M., Bellini, V., Rossi, T., Hálasz, G., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254550 · Cited by 40 · see citing works
Self-citation
Diagnostic Accuracy of North America Expert Consensus Statement on Reporting CT Findings in Patients Suspected of Having COVID-19 Infection: An Italian Single-Center Experience
Ciccarese, F., Coppola, F., Spinelli, D., Galletta, G. L., Lucidi, V., Paccapelo, A., Benedittis, C. D., Balacchi, C., & Golfieri, R. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200312 · Cited by 39 · see citing works
Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study
Ho, T. T., Park, J., Kim, T., Park, B., Lee, J., Kim, J. Y., Kim, K. B., Choi, S., Kim, Y. H., Lim, J., et al. (2021) · JMIR Medical Informatics · doi:10.2196/24973 · Cited by 39 · see citing works
The value of computed tomography in assessing the risk of death in COVID-19 patients presenting to the emergency room
Besutti, G., Ottone, M., Fasano, T., Pattacini, P., Iotti, V., Spaggiari, L., Bonacini, R., Nitrosi, A., Bonelli, E., Canovi, S., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07993-9 · Cited by 38 · see citing works
Admission chest CT score predicts 5-day outcome in patients with COVID-19
Study Group§, Mahdjoub, E., Mohammad, W., Lefevre, T., Debray, M., & Khalil, A. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-06118-y · Cited by 37 · see citing works
Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia
Khosravi, B., Aghaghazvini, L., Sorouri, M., Atashi, S. N., Abdollahi, M., Mojtabavi, H., Khodabakhshi, M., Motamedi, F., Azizi, F., Rajabi, Z., et al. (2020) · Heart & Lung · doi:10.1016/j.hrtlng.2020.10.005 · Cited by 37 · see citing works
Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network
Yoo, S., Yoon, S. H., Lee, J. H., Kim, K. H., Choi, H. I., Park, S. J., & Goo, J. M. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2020.0318 · Cited by 37 · see citing works
Adverse Initial CT Findings Associated with Poor Prognosis of Coronavirus Disease
Chon, Y., Kim, J. Y., Suh, Y. J., Lee, J. Y., Park, J. S., Moon, S. M., Lee, M. S., & Yi, J. (2020) · Journal of Korean Medical Science · doi:10.3346/jkms.2020.35.e316 · Cited by 35 · see citing works
Visual lung damage CT score at hospital admission of COVID-19 patients and 30-day mortality
Charpentier, E., Soulat, G., Fayol, A., Hernigou, A., Livrozet, M., Grand, T., Reverdito, G., al Haddad, J., Tran, K. D., Charpentier, A., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07938-2 · Cited by 35 · see citing works
Predictive indicators of severe COVID-19 independent of comorbidities and advanced age: a nested case−control study
Li, X., Marmar, T., Xu, Q., Tu, J., Yin, Y., Tao, Q., Chen, H., Shen, T., & Xu, D. (2020) · Epidemiology and Infection · doi:10.1017/s0950268820002502 · Cited by 33 · see citing works
A comprehensive review of imaging findings in COVID-19 - status in early 2021
Afshar‐Oromieh, A., Prosch, H., Schaefer‐Prokop, C. M., Bohn, K. P., Alberts, I., Mingels, C., Thurnher, M. M., Cumming, P. D., Shi, K., Peters, A. A., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05375-3 · Cited by 34 · see citing works
Prevalence and clinical outcomes of pleural effusion in COVID‐19 patients: A systematic review and meta‐analysis
Rathore, S. S., Hussain, N., Manju, A. H., Wen, Q., Tousif, S., Capriles, C. A. A., Woodbine, M. J. H., Rojas, G. A., Vatsavayi, P., Tera, C. R., et al. (2021) · Journal of Medical Virology · doi:10.1002/jmv.27301 · Cited by 32 · see citing works
Baricitinib as rescue therapy in a patient with COVID-19 with no complete response to sarilumab
Cingolani, A., Tummolo, A. M., Montemurro, G., Gremese, E., Larosa, L., Cipriani, M. C., Pasciuto, G., Liperoti, R., Murri, R., Pirronti, T., et al. (2020) · Infection · doi:10.1007/s15010-020-01476-7 · Cited by 32 · see citing works
Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany
Meiler, S., Schaible, J., Poschenrieder, F., Scharf, G., Zeman, F., Rennert, J., Pregler, B., Kleine, H., Stroszczynski, C., Zorger, N., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109256 · Cited by 32 · see citing works
A systematic review and meta-analysis of regional risk factors for critical outcomes of COVID-19 during early phase of the pandemic
Kim, H., Hwang, H., Hong, H., Yim, J., & Lee, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-89182-8 · Cited by 32 · see citing works
Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity
Aoki, R., Iwasawa, T., Hagiwara, E., Komatsu, S., Utsunomiya, D., & Ogura, T. (2021) · Japanese Journal of Radiology · doi:10.1007/s11604-020-01085-2 · Cited by 26 · see citing works
Pleural abnormalities in COVID-19: a narrative review
Saha, B. K., Chong, W. H., Austin, A., Kathuria, R., Datar, P. B., Shkolnik, B., Beegle, S. H., & Chopra, A. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-542 · Cited by 33 · see citing works
Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography
Morozov, S. P., Gombolevskiy, V. А., Chernina, V., Blokhin, I. A., Mokienko, O. A., Vlаdzimirskiy, A. V., Belevskiy, А. S., Николаевич, П. Д., Анатольевна, Л. М., Zayratyants, O. V., et al. (2020) · Tuberculosis and lung diseases · doi:10.21292/2075-1230-2020-98-6-7-14 · Cited by 30 · see citing works
Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the United States
Cury, R. C., Megyeri, I., Lindsey, T., Macedo, R., Batlle, J. C., Kim, S., Baker, B., Harris, R. J., & Clark, R. H. (2021) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2021200596 · Cited by 30 · see citing works
Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients
Rizzetto, F., Perillo, N., Artioli, D., Travaglini, F., Cuccia, A., Zannoni, S., Tombini, V., Domenico, S. L. D., Albertini, V., Bergamaschi, M., et al. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109650 · Cited by 30 · see citing works
Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19
Kitrungrotsakul, T., Chen, Q., Wu, H., Iwamoto, Y., Hu, H., Zhu, W., Chen, C., Xu, F., Zhou, Y., Lin, L., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3082527 · Cited by 30 · see citing works
The deep impact of novel CoVID-19 infection in an Orthopedics and Traumatology Department: the experience of the Piacenza Hospital.
Maniscalco, P., Poggiali, E., Quattrini, F., Ciatti, C., Magnacavallo, A., Caprioli, S., Vadacca, G. B., Michieletti, E., Cavanna, L., & Capelli, P. (2020) · Acta bio-medica dell'Ateneo parmense/Acta biomedica · doi:10.23750/abm.v91i2.9635 · Cited by 29 · see citing works
Self-citation

Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome
Annoni, A. D., Conte, E., Mancini, M. E., Gigante, C., Agalbato, C., Formenti, A., Muscogiuri, G., Mushtaq, S., Guglielmo, M., Baggiano, A., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11020265 · Cited by 14 · see citing works
Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19
Jung, H.-M., Yang, R., Gefter, W. B., Ghesu, F. C., Mailhé, B., Mansoor, A., Grbić, S., Comaniciu, D., Vogt, S. K., & Barbosa, E. J. M. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.3.034003 · Cited by 1 · see citing works
CT-based pathological lung volume and adverse outcomes of patients with Coronavirus Disease 2019 (COVID-19)
Morales-Jaramillo, L. M., Timaran, D. E., Mateo-Camacho, Y. S., Torres-Ramírez, C. A., Fuentes-Badillo, K. D., Morales-Domínguez, V., Punzo-Alcaraz, G., Tapia-Rangel, E. A., Feria-Arroyo, G. A., Parra-Guerrero, L. M., et al. (2023) · Anales de radiología, México/Anales de radiología, México · doi:10.24875/arm.21000095
Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients
Galzin, E., Roche, L., Vlachomitrou, A. S., Nempont, O., Carolus, H., Schmidt-Richberg, A., Jin, P., Rodrigues, P., Klinder, T., Richard, J., et al. (2022) · Research in Diagnostic and Interventional Imaging · doi:10.1016/j.redii.2022.100018 · Cited by 1 · see citing works
Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease
Palumbo, P., Palumbo, M. M., Bruno, F., Picchi, G., Iacopino, A., Acanfora, C., Sgalambro, F., Arrigoni, F., Ciccullo, A., Cosimini, B., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11112125 · Cited by 3 · see citing works

Authors: Colombi, D., Bodini, F. C., Petrini, M., Maffi, G., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E.
Affiliations: University of Parma (IT)
Corresponding author: Colombi, Davide
ORCID: Colombi, Davide
Bodini, Flavio C.
Petrini, Marcello
Maffi, Gabriele
Morelli, Nicola
Milanese, Gianluca
Silva, Mario
Sverzellati, Nicola
Michieletti, Emanuele
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2020-08
Volume: 296
Issue: 2
Pages: E86-E96
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2020201433
PMC: PMC7233411
OpenAlex: W3017116151
Cited by: 456
References: 33
Citations by year: 2026: 3 · 2025: 15 · 2024: 22 · 2023: 38 · 2022: 80 · 2021: 163 · 2020: 135
FWCI: 13.66
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Radiology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
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 › Neurology › Long-Term Effects of COVID-19
Keywords: chest CT, prognosis, intensive care unit admission, mortality, CT volumetry, prognostic factors, SARS-CoV-2, emergency department admission, risk stratification, SARS-CoV-2 serology
MeSH terms: Betacoronavirus, COVID-19, SARS-CoV-2, Aged, Emergency Service, Hospital, Female, Hospitalization, Humans, Intensive Care Units, Male, Middle Aged, Patient Admission, Pneumonia, Viral, Predictive Value of Tests, Prognosis, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, ROC Curve, Tomography, X-Ray Computed, Coronavirus Infections, Pandemics

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Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

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