Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome

Hosseiny, Melina; Kooraki, Soheil; Gholamrezanezhad, Ali; Reddy, Sravanthi; Myers, Lee

doi:10.2214/ajr.20.22969

Cited by 417 publications

(466 citation statements)

References 19 publications

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“…Elevated HCRP is an indicator of anti-virus inflammation, also served as a risk factor for malignant progression. The risk factors mentioned above are all in accordance with the results from observation studies [22][23][24][25][26] . Fibrosis on CT imaging is reported to be closely associated with the late outcome of COVID-19 pneumonia 27,28 .…”

Section: Discussionsupporting

confidence: 84%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2020

Preprint

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Background and purpose: The worldwide pandemic of coronavirus disease 2019 greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression.Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared.Results: Multivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893), Conclusions: Our deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

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Section: Discussionsupporting

confidence: 84%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2020

Preprint

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“…Indeed, lung fibrosis is well-documented in a substantial number of patients who have recovered from the infection of SARS-CoV or MERS-CoV [68, 69] and a group of influenza patients with severe acute diseases [20]. Emerging evidence has also suggested that COVID-19 survivors will likely exhibit persistent impairment of lung function and the development of pulmonary fibrosis [21, 70-74]. Data presented here suggests for the first time that excessive T RM cells are likely the cause of chronic lung diseases and pulmonary fibrosis following viral pneumonia in aged individuals.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the acute mortality caused by respiratory viral infections, evidence has suggested that survivors of primary viral pneumonia may display persistent impairment of lung function due to lung fibrosis [20]. Furthermore, it is predicted that a large proportion of elderly Corona Virus Disease 2019 (COVID-19) survivors will be prone to persistent impairment of lung function and pulmonary fibrosis as was observed in survivors of SARS and Middle East Respiratory Syndrome (MERS) [21]. At this time, there are no preventative means or therapeutic interventions available to mitigate and/or reverse lung fibrosis development following viral pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Tissue-resident CD8⁺T cells drive age-associated chronic lung sequelae following viral pneumonia

Sun

Goplen

2020

Preprint

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32Lower respiratory viral infections, such as influenza virus and severe acute respiratory syndrome 33 coronavirus 2 (SARS-CoV2) infections, often cause severe viral pneumonia in aged individuals. 34Here, we report that influenza viral pneumonia leads to chronic non-resolving lung pathology 35 and exaggerated accumulation of CD8 + tissue-resident memory T cells (TRM) in the respiratory 36 tract of aged hosts. TRM accumulation relies on elevated TGF-b present in aged tissues. Further, 37we show that TRM isolated from aged lungs lack a subpopulation characterized by expression of 38 molecules involved in TCR signaling and effector function. Consequently, TRM cells from aged 39 lungs were insufficient to provide heterologous protective immunity. Strikingly, the depletion of 40 CD8 + TRM cells dampens persistent chronic lung inflammation and ameliorates tissue fibrosis in 41 aged, but not young, animals. Collectively, our data demonstrate that age-associated TRM cell 42 malfunction supports chronic lung inflammatory and fibrotic sequelae following viral pneumonia 43 in aged hosts. 44 45 46 47 48

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“…12 Uncommon imaging findings Imaging features that are only rarely encountered include pneumothorax, cavitation, or lymphadenopathy. 13 Features such as pleural effusion, extensive tiny lung nodules, tree in bud, and lymphadenopathy may well suggest bacterial superinfection or an alternative diagnosis. 14…”

Section: Temporal Evolution Of Findingsmentioning

confidence: 99%

An update on COVID-19 for the radiologist - A British society of Thoracic Imaging statement

et al. 2020

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Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome

Cited by 417 publications

References 19 publications

Deep learning for predicting COVID-19 malignant progression

Deep learning for predicting COVID-19 malignant progression

Tissue-resident CD8⁺T cells drive age-associated chronic lung sequelae following viral pneumonia

An update on COVID-19 for the radiologist - A British society of Thoracic Imaging statement

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Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome

Cited by 417 publications

References 19 publications

Deep learning for predicting COVID-19 malignant progression

Deep learning for predicting COVID-19 malignant progression

Tissue-resident CD8+T cells drive age-associated chronic lung sequelae following viral pneumonia

An update on COVID-19 for the radiologist - A British society of Thoracic Imaging statement

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Tissue-resident CD8⁺T cells drive age-associated chronic lung sequelae following viral pneumonia