Lung perfusion changes in COVID-19 pneumonia: a dual energy computerized tomography study

Objective:There is limited and contradictory information about pulmonary perfusion changes detected with dual energy computed tomography (DECT) in COVID-19 cases. The purpose of this study was to define lung perfusion changes in COVID-19 cases with DECT, as well as to reveal any possible links between perfusion changes and laboratory findings.Methods:Patients who had a positive RT-PCR for SARS-CoV-2 and a contrast-enhanced chest DECT examination were included in the study. The pattern and severity of perfusion deficits were evaluated, as well as the relationships between perfusion deficit severity and laboratory results and CT severity ratings. The paired t-test, Wilcoxon test, and Student’s t-test were used to examine the changes in variables and perfusion deficits. p < 0.05 was regarded as statistically significant.Results:Study population consisted of 40 patients. Mean age was 60.73 ± 14.73 years. All of the patients had perfusion deficits at DECT images. Mean perfusion deficit severity score of the population was 8.45 ± 4.66 (min.-max, 1–19). In 24 patients (60%), perfusion deficits and parenchymal lesions matched completely. In 15 patients (37.5%), there was partial match. D dimer, CRP levels, CT severity score, and perfusion deficit severity score all had a positive correlationConclusions:Perfusion deficits are seen not only in opacification areas but also in parenchyma of normal appearance. The CT severity score, CRP, D-dimer, and SpO2 levels of the patients were determined to be related with perfusion deficit severity.Advances in knowledge:Findings of the current study may confirm the presence of micro-thrombosis in COVID-19 pneumonia.     

Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients

PurposeThis study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing.MethodsSeventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis.ResultsFifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern.ConclusionAutomated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.     

基于机器学习的CT定量指标与新型冠状病毒肺炎临床分型及肺损伤严重程度的相关性研究

目的:探讨胸部CT定量指标在新型冠状病毒肺炎（COVID-19）临床分型及肺损伤严重程度评价中的价值。方法:回顾性分析华中科技大学同济医学院附属同济医院2020年1月1日至2020年4月1日COVID-19确诊的438例患者的临床及CT影像资料。临床分型为普通型146例、重型247例、危重型45例。使用人工智能（AI）...     

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

ObjectiveTo develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.Materials and MethodsClinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.ResultsAmong 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively.ConclusionCT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.     

Utility of visual coronary artery calcification on non-cardiac gated thoracic CT in predicting clinical severity and outcome in COVID-19

BackgroundAssessment of visual-coronary artery calcification on non-cardiac gated CT in COVID-19 patients could provide an objective approach to rapidly identify and triage clinically severe patients for early hospital admission to avert worse prognosis.PurposeTo ascertain the role of semi-quantitative scoring in visual-coronary artery calcification score (V-CACS) for predicting the clinical severity and outcome in patients with COVID-19.Materials and methodsWith institutional review board approval this study included 67 COVID-19 confirmed patients who underwent non-cardiac gated CT chest in an inpatient setting. Two blinded radiologist (Radiologist-1 &2) assessed the V-CACS, CT Chest severity score (CT-SS). The clinical data including the requirement for oxygen support, assisted ventilation, ICU admission and outcome was assessed, and patients were clinically subdivided depending on clinical severity. Logistic regression analyses were performed to identify independent predictors. ROC curves analysis is performed for the assessment of performance and Pearson correlation were performed to looks for the associations.ResultsV-CACS cut off value of 3 (82.67% sensitivity and 54.55% specificity; AUC 0.75) and CT-SS with a cut off value of 21.5 (95.7% sensitivity and 63.6% specificity; AUC 0.87) are independent predictors for clinical severity and also the need for ICU admission or assisted ventilation. The pooling of both CT-SS and V-CACS (82.67% sensitivity and 86.4% specificity; AUC 0.92) are more reliable in terms of predicting the primary outcome of COVID-19 patients. On regression analysis, V-CACS and CT-SS are individual independent predictors of clinical severity in COVID-19 (Odds ratio, 1.72; 95% CI, 0.99–2.98; p = 0.05 and Odds ratio, 1.22; 95% CI, 1.08–1.39; p = 0.001 respectively). The area under the curve (AUC) for pooled V-CACS and CT-SS was 0.96 (95% CI 0.84–0.98) which correctly predicted 82.1% cases.ConclusionLogistic regression model using pooled Visual-Coronary artery calcification score and CT Chest severity score in non-cardiac gated CT can predict clinical severity and outcome in patients with COVID-19.     

A case–control evaluation of pulmonary and extrapulmonary findings of incidental asymptomatic COVID-19 infection on FDG PET-CT

Objectives:To describe the findings of incidental asymptomatic COVID-19 infection on FDG PET-CT using a case–control design.Methods:Incidental pulmonary findings suspicious of asymptomatic COVID-19 infection on FDG PET-CT were classified as a confirmed (positive RT-PCR test) or suspected case (no/negative RT-PCR test). Control cases were identified using a 4:1 control:case ratio. Pulmonary findings were re-categorised by two reporters using the BSTI classification. SUV metrics in ground glass opacification (GGO)/consolidation (where present), background lung, intrathoracic nodes, liver, spleen and bone marrow were measured.Results:7/9 confirmed and 11/15 suspected cases (COVID-19 group) were re-categorised as BSTI 1 (classic/probable COVID-19) or BSTI 2 (indeterminate COVID-19); 0/96 control cases were categorised as BSTI 1. Agreement between two reporters using the BSTI classification was almost perfect (weighted κ = 0.94). SUV_max GGO/consolidation (5.1 vs 2.2; p < 0.0001) and target-to-background ratio, normalised to liver SUV_mean (2.4 vs 1.0; p < 0.0001) were higher in the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases. SUV_max GGO/consolidation discriminated between the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases with high accuracy (AUC = 0.93). SUV metrics were higher (p < 0.05) in the COVID-19 group vs control cases in the lungs, intrathoracic nodes and spleen.Conclusion:Asymptomatic COVID-19 infection on FDG PET-CT is characterised by bilateral areas of FDG avid (intensity > x2 liver SUV_mean) GGO/consolidation and can be identified with high interobserver agreement using the BSTI classification. There is generalised background inflammation within the lungs, intrathoracic nodes and spleen.Advances in knowledge:Incidental asymptomatic COVID-19 infection on FDG PET-CT, characterised by bilateral areas of ground glass opacification and consolidation, can be identified with high reproducibility using the BSTI classification. The intensity of associated FDG uptake (>x2 liver SUV_mean) provides high discriminative ability in differentiating such cases from pulmonary findings in a non-COVID-19 pattern. Asymptomatic COVID-19 infection causes a generalised background inflammation within the mid-lower zones of the lungs, hilar and central mediastinal nodal stations, and spleen on FDG PET-CT.     

Complications in COVID-19 patients: Characteristics of pulmonary embolism

ObjectiveThe purpose of this study is to evaluate chest CT imaging features, clinical characteristics, laboratory values of COVID-19 patients who underwent CTA for suspected pulmonary embolism. We also examined whether clinical, laboratory or radiological characteristics could be associated with a higher rate of PE.Materials and methodsThis retrospective study included 84 consecutive patients with laboratory-confirmed SARS-CoV-2 who underwent CTA for suspected PE. The presence and localization of PE as well as the type and extent of pulmonary opacities on chest CT exams were examined and correlated with the information on comorbidities and laboratory values for all patients.ResultsOf the 84 patients, pulmonary embolism was discovered in 24 patients. We observed that 87% of PE was found to be in lung parenchyma affected by COVID-19 pneumonia. Compared with no-PE patients, PE patients showed an overall greater lung involvement by consolidation (p = 0.02) and GGO (p < 0.01) and a higher level of D-Dimer (p < 0,01). Moreover, the PE group showed a lower level of saturation (p = 0,01) and required more hospitalization (p < 0,01).ConclusionOur study showed a high incidence of PE in COVID-19 pneumonia. In 87% of patients, PE was found in lung parenchyma affected by COVID-19 pneumonia with a worse CT severity score and a greater number of lung lobar involvement compared with non-PE patients. CT severity, lower level of saturation, and a rise in D-dimer levels could be an indication for a CTPA.Advances in knowledgeCertain findings of non-contrast chest CT could be an indication for a CTPA.     

Structured reporting of chest CT provides high sensitivity and specificity for early diagnosis of COVID-19 in a clinical routine setting

Objectives:Although chest CT has been widely used in patients with COVID-19, its role for early diagnosis of COVID-19 is unclear. We report the diagnostic performance of chest CT using structured reporting in a routine clinical setting during the early phase of the epidemic in Germany.Methods:Patients with clinical suspicion of COVID-19 and moderate-to-severe symptoms were included in this retrospective study. CTs were performed and reported before RT-PCR results (reference standard) became available. A structured reporting system was used that concluded in a recently described five-grade score (“CO-RADS”), indicating the level of suspicion for pulmonary involvement of COVID-19 from 1 = very low to 5 = very high. Structured reporting was performed by three Radiologists in consensus.Results:In 96 consecutive patients (50 male, mean age 64), RT-PCR was positive in 20 (21%) cases. CT features significantly more common in RT-PCR-positive patients were ground-glass opacities as dominant feature, crazy paving, hazy margins of opacities, and multifocal bilateral distribution (p < 0.05). Using a cut-off point between CO-RADS 3 and 4, sensitivity was 90%, specificity 91%, positive predictive value 72%, negative predictive value 97%, and accuracy 91%. ROC analysis showed an AUC of 0.938.Conclusions:Structured reporting of chest CT with a five-grade scale provided accurate diagnosis of COVID-19. Its use was feasible and helpful in clinical routine.Advances in knowledge:Chest CT with structured reporting may be a provisional diagnostic alternative to RT-PCR testing for early diagnosis of COVID-19, especially when RT-PCR results are delayed or test capacities are limited.     

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients

BackgroundThe aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome.MethodsConsecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge.ResultsSeventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = −0.234, p = 0.04) and increased semi-consolidation ratio (rho = −0.303, p = 0.008).Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge.ConclusionQuantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.     

Características en tomografía computarizada de la neumonía por coronavirus-2019 (COVID-19) en tres grupos de pacientes iraníes: estudio de un solo centro

Introduction and ObjectivesThe pivotal role of chest computed tomographic (CT) to diagnosis and prognosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. This study was conducted to assess the CT features in confirmed cases with COVID-19.Materials and MethodsRetrospectively, initial chest CT data of 363 confirmed cases with COVID-19 were reviewed. All subjects were stratified into three groups based on patients’ clinical outcomes; non-critical group (n=194), critical group (n=65), and death group (n=104). The detailed of CT findings were collected from patients’ medical records and then evaluated for each group. In addition, multinomial logistic regression was used to analyze risk factors according to CT findings in three groups of patients with COVID-19.ResultsCompared with the non-critical group, mixed ground-glass opacities (GGO) and consolidation lesion, pleural effusion lesion, presence of diffuse opacity in cases, more than 2 lobes involved and opacity scores were significantly higher in the critical and death groups (P<0.05). Having more mixed GGO with consolidation, pleural effusion, lack of pure GGO, more diffuse opacity, involvement of more than 2 lobes and high opacity score identified as independent risk factors of critical and death groups.ConclusionCT images of non-critical, critical and death groups with COVID-19 had definite characteristics. CT examination plays a vital role in managing the current COVID-19 outbreak, for early detection of COVID-19 pneumonia. In addition, initial CT findings may be useful to stratify patients, which have a potentially important utility in the current global medical situation.     

Typical chest CT features can determine the severity of COVID-19: A systematic review and meta-analysis of the observational studies

BackgroundIt remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19.MethodsPubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger's test.ResultsA total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81–74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by crazy paving (OR 7.60, 95% CI 3.82–15.14), linear opacity (OR 3.27, 95% CI 1.10–9.70), and GGO (OR 1.37, 95% CI 1.08–1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85–6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74–6.79).ConclusionsOur meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.     

Pulmonary embolism severity before and during the COVID-19 pandemic

Objectives:Early in the coronavirus 2019 (COVID-19) pandemic, a high frequency of pulmonary embolism was identified. This audit aims to assess the frequency and severity of pulmonary embolism in 2020 compared to 2019.Methods:In this retrospective audit, we compared computed tomography pulmonary angiography (CTPA) frequency and pulmonary embolism severity in April and May 2020, compared to 2019. Pulmonary embolism severity was assessed with the Modified Miller score and the presence of right heart strain was assessed. Demographic information and 30-day mortality was identified from electronic health records.Results:In April 2020, there was a 17% reduction in the number of CTPA performed and an increase in the proportion identifying pulmonary embolism (26%, n = 68/265 vs 15%, n = 47/320, p < 0.001), compared to April 2019. Patients with pulmonary embolism in 2020 had more comorbidities (p = 0.026), but similar age and sex compared to 2019. There was no difference in pulmonary embolism severity in 2020 compared to 2019, but there was an increased frequency of right heart strain in May 2020 (29 vs 12%, p = 0.029). Amongst 18 patients with COVID-19 and pulmonary embolism, there was a larger proportion of males and an increased 30 day mortality (28% vs 6%, p = 0.008).Conclusion:During the COVID-19 pandemic, there was a reduction in the number of CTPA scans performed and an increase in the frequency of CTPA scans positive for pulmonary embolism. Patients with both COVID-19 and pulmonary embolism had an increased risk of 30-day mortality compared to those without COVID-19.Advances in knowledge:During the COVID-19 pandemic, the number of CTPA performed decreased and the proportion of positive CTPA increased. Patients with both pulmonary embolism and COVID-19 had worse outcomes compared to those with pulmonary embolism alone.     

Differential diagnosis between the coronavirus disease 2019 and Streptococcus pneumoniae pneumonia by thin-slice CT features

ObjectiveThe chest computed tomography (CT) features of coronavirus disease 2019 (COVID-19) and Streptococcus pneumoniae pneumonia (S. pneumoniae pneumonia) were compared to provide further evidence for the differential imaging diagnosis of patients with these two types of pneumonia.MethodsClinical information and chest CT data of 149 COVID-19 patients between January 9, 2020 and March 15, 2020 and 97 patients with S. pneumoniae pneumonia between January 23, 2011 and March 18, 2020 in Zhongnan Hospital of Wuhan University were retrospectively analyzed. In addition, CT features were comparatively analyzed.ResultsAccording to the chest CT images, the probability of lung segmental and lobar pneumonia in S. pneumoniae pneumonia was higher than that in COVID-19(P<0.001); the probabilities of ground-glass opacity (GGO), the “crazy paving” sign, and abnormally thickened interlobular septa in COVID-19 were higher than those in S. pneumoniae pneumonia(P = 0.005, P<0.001, P<0.001, respectively); and the probabilities of consolidation lesions, bronchial wall thickening, centrilobular nodules, and pleural effusion in S. pneumoniae pneumonia were higher than those in COVID-19 (P<0.001, P = 0.001, P = 0.003, P = 0.001, respectively).ConclusionThe findings of GGO, the crazy paving sign, and abnormally thickened interlobular septa on chest CT were significantly higher in COVID-19 than S. pneumoniae pneumonia. The most important differential points on chest CT signs between COVID-19 and S. pneumoniae pneumonia were whether disease lesions were distributed in entire lung lobes and segments and whether the crazy paving sign, interlobular septal thickening, and consolidation lesions were found.     

Neuroimaging Findings in Patients with COVID-19: A Systematic Review and Meta-Analysis

ObjectiveCentral nervous system involvement in coronavirus disease 2019 (COVID-19) has been increasingly reported. We performed a systematic review and meta-analysis to evaluate the incidence of radiologically demonstrated neurologic complications and detailed neuroimaging findings associated with COVID-19.Materials and MethodsA systematic literature search of MEDLINE/PubMed and EMBASE databases was performed up to September 17, 2020, and studies evaluating neuroimaging findings of COVID-19 using brain CT or MRI were included. Several cohort-based outcomes, including the proportion of patients with abnormal neuroimaging findings related to COVID-19 were evaluated. The proportion of patients showing specific neuroimaging findings was also assessed. Subgroup analyses were also conducted focusing on critically ill COVID-19 patients and results from studies that used MRI as the only imaging modality.ResultsA total of 1394 COVID-19 patients who underwent neuroimaging from 17 studies were included; among them, 3.4% of the patients demonstrated COVID-19-related neuroimaging findings. Olfactory bulb abnormalities were the most commonly observed (23.1%). The predominant cerebral neuroimaging finding was white matter abnormality (17.6%), followed by acute/subacute ischemic infarction (16.0%), and encephalopathy (13.0%). Significantly more critically ill patients had COVID-19-related neuroimaging findings than other patients (9.1% vs. 1.6%; p = 0.029). The type of imaging modality used did not significantly affect the proportion of COVID-19-related neuroimaging findings.ConclusionAbnormal neuroimaging findings were occasionally observed in COVID-19 patients. Olfactory bulb abnormalities were the most commonly observed finding. Critically ill patients showed abnormal neuroimaging findings more frequently than the other patient groups. White matter abnormalities, ischemic infarctions, and encephalopathies were the common cerebral neuroimaging findings.     

基于感染防控的新型冠状病毒肺炎低剂量CT扫描方案的临床应用研究

目的 探讨感染防控下新型冠状病毒（COVID-19）肺炎低剂量CT扫描方案的临床应用。方法 分析2020年1月20日到2020年2月28日在华中科技大学同济医学院附属协和医院确诊的COVID-19病例的CT影像140例,将患者分为低剂量组和常规剂量组。低剂量组（120 kV,31 mAs）70例,其中轻症51例,重症15例,危重症4例;常规剂量组（120 kV,自适应毫安秒）70例,其中轻症48例,重症17例,危重症5例。比较两组病例有效剂量（E）、图像信噪比（SNR）、对噪比（CNR）,由两位高、中年资影像诊断医生对图像进行主观评分。结果 低剂量组和常规剂量组E比较,差异有统计学意义（t=-48.343,P<0.05）。低剂量组的SNR和常规剂量组的SNR比较,差异无统计学意义（P>0.05）。低剂量组的CNR和常规剂量组的CNR比较,差异无统计学意义（P>0.05）。主观评价均满足诊断需求,轻症患者中,低剂量组和常规剂量组的影像主观评分比较,差异无统计学意义（P>0.05）;重症与危重患者中,低剂量组和常规剂量组的影像主观评分比较,差异有统计学意义（t=-2.781,P<0.05）;但两组图像均符合诊断要求。结论 对COVID-19患者进行低剂量CT扫描,可在满足诊断需求的前提下降低患者辐射剂量。