Initial HRCT findings of novel influenza A (H1N1) infection

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients

Background

The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.

Methods

One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.

Results

In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOI_CT) and the percentage of infection (POI_CT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOI_CT and clinical features, including age, cluster of differentiation 4 (CD4)⁺ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOI_CT, POI_CT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively).

Conclusions

Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients

BACKGROUND

The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.

METHODS

One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.

RESULTS

In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOI_CT) and the percentage of infection (POI_CT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOI_CT and clinical features, including age, cluster of differentiation 4 (CD4)⁺ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOI_CT, POI_CT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively).

CONCLUSIONS

Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

Chest computed tomography scan findings of coronavirus disease 2019 (COVID-19) patients: a comprehensive systematic review and meta-analysis

INTRODUCTION

Numerous cases of pneumonia caused by coronavirus disease 2019 (COVID-19) were reported in Wuhan, China. Chest computed tomography (CT) scan is highly important in the diagnosis and follow-up of lung disease treatment. The present meta-analysis was performed to evaluate chest CT scan findings in COVID-19 patients.

MATERIAL AND METHODS

All research steps were taken according to the Meta-Analysis of Observational Studies In Epidemiology (MOOSE) protocol and the final report was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We registered this review at the International Prospective Register of Systematic Reviews (PROSPERO, CRD42019127858).

RESULTS

Forty eligible studies including 4598 patients with COVID-19 were used for meta-analysis. The rate of positive chest CT scan in patients with COVID-19 was 94.5% (95% CI: 91.7-96.3). Bilateral lung involvement, pure ground-glass opacity (GGO), mixed (GGO pulse consolidation or reticular), consolidation, reticular, and presence of nodule findings in chest CT scan of COVID-19 pneumonia patients were respectively estimated to be 79.1% (95% CI: 70.8-85.5), 64.9% (95% CI: 54.1-74.4), 49.2% (95% CI: 35.7-62.8), 30.3% (95% CI: 19.6-43.6), 17.0% (95% CI: 3.9-50.9) and 16.6% (95% CI: 13.6-20.2). The distribution of lung lesions in patients with COVID-19 pneumonia was peripheral (70.0% [95% CI: 57.8-79.9]), central (3.9% [95% CI: 1.4-10.6]), and peripheral and central (31.1% [95% CI: 19.5-45.8]). The pulmonary lobes most commonly involved were the right lower lobe (86.5% [95% CI:57.7-96.8]) and left lower lobe (81.0% [95% CI: 50.5-94.7]).

CONCLUSIONS

The most important outcomes in chest CT scan of patients with COVID-19 pneumonia were bilateral lung involvement, GGO or mixed (GGO pulse consolidation or reticular) patterns, thickened interlobular septa, vascular enlargement, air bronchogram sign, peripheral distribution, and left and right lower lobes involvement. Our study showed that chest CT scan has high sensitivity in the diagnosis of COVID-19, and may therefore serve as a standard method for diagnosis of COVID-19.

Evaluating antibody response pattern in asymptomatic virus infected pregnant females: Human well-being study

The ongoing SARS-CoV-2 pandemic infecting millions of people globally has given rise to serious public health threats. The need for early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in asymptomatic pregnant women is compelling to detect vertical transmission timely. Here, 11 SARS-CoV-2 asymptomatic pregnant cases from Wuhan China were investigated. All the patients were initially tested negative for SARS-CoV-2 on RT-PCR, so a chest CT scan was performed. Also, serum antibody (IgM and IgG) titers were estimated. CT scan of patients revealed typical abnormalities related to SARS-CoV-2, indicating ground-glass opacity and infection lesions suggesting viral pneumonia. Elevated IgM and IgG antibodies levels (p < 0.001) were also noticed in infected patients. Hence, CT imaging and serum antibody response are valuable in the early detection of SARS-CoV-2 in asymptomatic pregnant patients. These might serve as prognostic markers for healthcare professionals, in RT-PCR negative patients, to assess the effect of given treatment by chest CT.

COVID-19 or non-COVID viral pneumonia: How to differentiate based on the radiologic findings?

Influenza viruses were responsible for most adult viral pneumonia. Presently, coronavirus disease 2019 (COVID-19) has evolved into serious global pandemic. COVID-19 outbreak is expected to persist in months to come that will be synchronous with the influenza season. The management, prognosis, and protection for these two viral pneumonias differ considerably and differentiating between them has a high impact on the patient outcome. Reverse transcriptase polymerase chain reaction is highly specific but has suboptimal sensitivity. Chest computed tomography (CT) has a high sensitivity for detection of pulmonary disease manifestations and can play a key-role in diagnosing COVID-19. We reviewed 47 studies and delineated CT findings of COVID-19 and influenza pneumonia. The differences observed in the chest CT scan can be helpful in differentiation. For instance, ground glass opacities (GGOs), as the most frequent imaging finding in both diseases, can differ in the pattern of distribution. Peripheral and posterior distribution, multilobular distribution, pure or clear margin GGOs were more commonly reported in COVID-19, whereas central or peri-bronchovascular GGOs and pure consolidations were more seen in influenza A (H1N1). In review of other imaging findings, further differences were noticed. Subpleural curvilinear lines, sugar melted sign, intra-lesional vascular enlargement, reverse halo sign, and fibrotic bands were more reported in COVID-19 than H1N1, while air space nodule, tree-in-bud, bronchiectasia, pleural effusion, and cavitation were more seen in H1N1. This delineation, when combined with clinical manifestations and laboratory results may help to differentiate these two viral infections.

The role of imaging in 2019 novel coronavirus pneumonia (COVID-19)

Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.

Chest CT-based differential diagnosis of 28 patients with suspected corona virus disease 2019 (COVID-19)

OBJECTIVES

The chest CT findings that can distinguish patients with corona virus disease 2019 (COVID-19) from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative have not previously been described in detail. The purpose of this study was to determine the distinctions among patients with COVID-19 by comparing the imaging findings of patients with suspected confirmed COVID-19 and those of patients initially suspected to have COVID-19 who were ultimately negative for the disease.

METHODS

28 isolated suspected in-patients with COVID-19 were enrolled in this retrospective study from January 22, 2020 to February 6, 2020. 12 patients were confirmed to have positive severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) RNA results, and 16 patients had negative results. The thin-section CT imaging findings and clinical and laboratory data of all the patients were evaluated.

RESULTS

There were no significant differences between the 12 confirmed COVID-19 (SARS-Cov-2-positive) patients and 16 SARS-CoV-2-negative patients in epidemiology and most of the clinical features or laboratory data. The CT images showed that the incidence of pure/mixed ground-glass opacities (GGOs) was not different between COVID-19 and SARS-CoV-2-negative patients [9/12 (75.0%) vs 10/16 (62.5%), p = 0.687], but pure/mixed GGOs in the peripheral were more common in patients with COVID-19 [11/12 (91.7%) vs 6/16 (37.5%), p = 0.006]. There were no significant differences in the number of lesions, bilateral lung involvement, large irregular/patchy opacities, rounded opacities, linear opacities, crazy-paving patterns, halo signs, interlobular septal thickening or air bronchograms.

CONCLUSIONS

Although peripheral pure/mixed GGOs on CT may help distinguish patients with COVID-19 from clinically suspected but negative patients, CT cannot replace RT-PCR testing.

ADVANCES IN KNOWLEDGE

Peripheral pure/mixed GGOs on-chest CT findings can be helpful in distinguishing patients with COVID-19 from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative.

Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis

OBJECTIVE

To determine the consistency between CT findings and real-time reverse transcription-polymerase chain reaction (RT-PCR) and to investigate the relationship between CT features and clinical prognosis in COVID-19.

METHODS

The clinical manifestations, laboratory parameters, and CT imaging findings were analyzed in 34 COVID-19 patients, confirmed by RT-PCR from January 20 to February 4 in Hainan Province. CT scores were compared between the discharged patients and the ICU patients.

RESULTS

Fever (85%) and cough (79%) were most commonly seen. Ten (29%) patients demonstrated negative results on their first RT-PCR. Of the 34 (65%) patients, 22 showed pure ground-glass opacity. Of the 34 (50%) patients, 17 had five lobes of lung involvement, while the 23 (68%) patients had lower lobe involvement. The lesions of 24 (71%) patients were distributed mainly in the subpleural area. The initial CT lesions of ICU patients were distributed in both the subpleural area and centro-parenchyma (80%), and the lesions were scattered. Sixty percent of ICU patients had five lobes involved, while this was seen in only 25% of the discharged patients. The lesions of discharged patients were mainly in the subpleural area (75%). Of the discharged patients, 62.5% showed pure ground-glass opacities; 80% of the ICU patients were in the progressive stage, and 75% of the discharged patients were at an early stage. CT scores of the ICU patients were significantly higher than those of the discharged patients.

CONCLUSION

Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. The initial features in CT may be associated with prognosis.

KEY POINTS

• Chest CT is valuable for the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. • The early CT findings of COVID-19 in ICU patients differed from those of discharged patients.

Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia

Objectives

To investigate the different CT characteristics which may distinguish influenza from 2019 coronavirus disease (COVID-19).

Methods

A total of 13 confirmed patients with COVID-19 were enrolled from January 16, 2020, to February 25, 2020. Furthermore, 92 CT scans of confirmed patients with influenza pneumonia, including 76 with influenza A and 16 with influenza B, scanned between January 1, 2019, to February 25, 2020, were retrospectively reviewed. Pulmonary lesion distributions, number, attenuation, lobe predomination, margin, contour, ground-glass opacity involvement pattern, bronchial wall thickening, air bronchogram, tree-in-bud sign, interlobular septal thickening, intralobular septal thickening, and pleural effusion were evaluated in COVID-19 and influenza pneumonia cohorts.

Results

Peripheral and non-specific distributions in COVID-19 showed a markedly higher frequency compared with the influenza group (p < 0.05). Most lesions in COVID-19 showed balanced lobe localization, while in influenza pneumonia they were predominantly located in the inferior lobe (p < 0.05). COVID-19 presented a clear lesion margin and a shrinking contour compared with influenza pneumonia (p < 0.05). COVID-19 had a patchy or combination of GGO and consolidation opacities, while a cluster-like pattern and bronchial wall thickening were more frequently seen in influenza pneumonia (p < 0.05). The lesion number and attenuation, air bronchogram, tree-in-bud sign, interlobular septal thickening, and intralobular septal thickening were not significantly different between the two groups (all p > 0.05).

Conclusions

Though viral pneumonias generally show similar imaging features, there are some characteristic CT findings which may help differentiating COVID-19 from influenza pneumonia.

Key Points

• CT can play an early warning role in the diagnosis of COVID-19 in the case of no epidemic exposure.

• CT could be used for the differential diagnosis of influenza and COVID-19 with satisfactory accuracy.

• COVID-19 had a patchy or combination of GGO and consolidation opacities with peripheral distribution and balanced lobe predomination.

Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia

BackgroundThe chest CT findings of patients with 2019 Novel Coronavirus (2019-nCoV) pneumonia have not previously been described in detail.PurposeTo investigate the clinical, laboratory, and imaging findings of emerging 2019-nCoV pneumonia in humans.Materials and MethodsFifty-one patients (25 men and 26 women; age range 16-76 years) with laboratory-confirmed 2019-nCoV infection by using real-time reverse transcription polymerase chain reaction underwent thin-section CT. The imaging findings, clinical data, and laboratory data were evaluated.ResultsFifty of 51 patients (98%) had a history of contact with individuals from the endemic center in Wuhan, China. Fever (49 of 51, 96%) and cough (24 of 51, 47%) were the most common symptoms. Most patients had a normal white blood cell count (37 of 51, 73%), neutrophil count (44 of 51, 86%), and either normal (17 of 51, 35%) or reduced (33 of 51, 65%) lymphocyte count. CT images showed pure ground-glass opacity (GGO) in 39 of 51 (77%) patients and GGO with reticular and/or interlobular septal thickening in 38 of 51 (75%) patients. GGO with consolidation was present in 30 of 51 (59%) patients, and pure consolidation was present in 28 of 51 (55%) patients. Forty-four of 51 (86%) patients had bilateral lung involvement, while 41 of 51 (80%) involved the posterior part of the lungs and 44 of 51 (86%) were peripheral. There were more consolidated lung lesions in patients 5 days or more from disease onset to CT scan versus 4 days or fewer (431 of 712 lesions vs 129 of 612 lesions; P < .001). Patients older than 50 years had more consolidated lung lesions than did those aged 50 years or younger (212 of 470 vs 198 of 854; P < .001). Follow-up CT in 13 patients showed improvement in seven (54%) patients and progression in four (31%) patients.ConclusionPatients with fever and/or cough and with conspicuous ground-glass opacity lesions in the peripheral and posterior lungs on CT images, combined with normal or decreased white blood cells and a history of epidemic exposure, are highly suspected of having 2019 Novel Coronavirus (2019-nCoV) pneumonia.© RSNA, 2020.

Thin-Section Chest CT Imaging of COVID-19 Pneumonia: A Comparison Between Patients with Mild and Severe Disease

PURPOSE

To compare radiologic characteristics of coronavirus disease 2019 (COVID-19) pneumonia at thin-section CT on admission between patients with mild and severe disease.

MATERIALS AND METHODS

Seventy patients with COVID-19 pneumonia who were admitted to Zhongnan Hospital of Wuhan University between January 20, 2020 and January 27, 2020 were enrolled. On the basis of the World Health Organization guidelines, 50 patients were categorized with the mild form and 20 with the severe form based on clinical conditions. Imaging features, clinical, and laboratory data were reviewed and compared.

RESULTS

Patients with the severe form (median age, 65 years; interquartile range [IQR]: 54.75-75.00 years) were older than those with the mild form of disease (median age, 42.5 years; IQR: 32.75-58.50 years) (P < .001). Patients with the severe form of disease had more lung segments involved (median number of segments: 17.5 vs 7.5, P ≤ .001) and also larger opacities (median number of segments with opacities measuring 3 cm to less than 50% of the lung segment: 5.5 vs 2.0, P = .006; ≥ 50% of lung segment: 7.5 vs 0.0, P < .001). They also had more interlobular septal thickening (75% vs 28%, P < .001), higher prevalence of air bronchograms (70% vs 32%, P = .004), and pleural effusions (40% vs 14%, P = .017).

CONCLUSION

Ground-glass opacities with or without consolidation in a peripheral and basilar predominant distribution were the most common findings in COVID-19 pneumonia. Patients with the severe form of the disease had more extensive opacification of the lung parenchyma than did patients with mild disease. Interlobular septal thickening, air bronchograms, and pleural effusions were also more prevalent in severe COVID-19.© RSNA, 2020.

Longitudinal changes of pneumonia complicating novel influenza A (H1N1) by high-resolution computed tomography

Purpose

To assess lung lesions in patients with pneumonia complicating novel influenza A (H1N1) by serial high-resolution computed tomography (HRCT) during the early, progressive and convalescent stages.

Samples and methods

Serial HRCT scans in 39 patients with pneumonia complicating novel influenza A (H1N1) were reviewed for predominant patterns of lung abnormalities as well as distribution and extent of involvement. Longitudinal changes were assessed at different time points.

Results

In the early stage, the most common HRCT finding was patchy ground-glass opacity (GGO) (n = 4, 54.7%). In the progressive stage, bilaterally distributed GGO mixed with consolidation was the most commonly observed feature (n = 28, 71.8%). The diffuse pattern deteriorated to a peak (n = 17, 43.6%) at this stage. In the convalescent stage, the most common finding was fibrosis (n = 25, 64.1%). Averagely, fibrosis was observed at d 18.5 ± 6.4 after the onset of symptoms. Three patterns of longitudinal changes of the lesions were observed, including: type 1, improvement after deterioration; type 2, concurrent improvement and deterioration followed by improvement; and type 3, gradual improvement. Type 1 was the more common pattern (n = 27, 69.2%). Complete serial HRCT scans from initial and final scan were obtained in 24 patients, and the mean CT score peaked at d 8–14 of the illness.

Conclusion

HRCT may play a role in detecting and characterizing pulmonary lesions for the cases of pneumonia complicating influenza A. In addition, it may contribute to monitoring longitudinal changes of pneumonia and assessing therapeutic response.

Chest X-ray and CT findings of early H7N9 avian influenza cases

BACKGROUND

The H7N9 strain of bird flu is a new type of avian flu that was identified at the end of March 2013. The disease is concerning because most patients have become severely ill.

PURPOSE

To study the X-ray and computed tomography (CT) findings of early H7N9 avian influenza cases.

MATERIAL AND METHODS

Chest radiography and CT were performed in six patients with H7N9 avian influenza within 1-20 days after onset. The CT examinations included conventional spiral CT and high-resolution CT. The findings on the radiography and CT images were analyzed.

RESULTS

Abnormal X-ray and CT findings were present in all of the patients. All of the cases had acute onset. In the early stage, the right lung was more commonly affected (particularly in the right upper and middle lobes). The lesions rapidly expanded to the entire lungs and were characterized primarily by ground-glass opacities (GGOs) combined with consolidation. Diffuse GGO was observed in all six cases (1 was symmetric, and 5 were non-symmetric). Local consolidation was found in four cases, and lobar consolidation was found in two cases. Normal lung tissue was observed between the lesions. Pleural thickening was common and was combined with pleural/pericardial effusion or mediastinal lymph node enlargement. Reticular changes, centrilobular nodules, and the tree-in-bud sign were observed in some cases, but reticular changes, bronchial wall thickening, and hyperinflation were not found.

CONCLUSION

Radiological changes associated with both acute pneumonia and acute interstitial inflammation were observed in early H7N9 avian influenza cases. Serial chest X-rays were useful for the diagnosis and severity assessment of the disease. CT may provide a more accurate assessment of the lung pathology.

Clinical Manifestations

The incubation period after infection of Influenza A(H1N1) is usually 1–7 days, being longer than common influenza and bird flu.