Preliminary CT findings of coronavirus disease 2019 (COVID-19)

ObjectivesCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This paper aims to examine the CT imaging characteristics of COVID-19.MethodsWe evaluated CT images obtained between 10 January 2019 and 16 February 2020 at Taihe Hospital. Scans were conducted 2–6 times per patient and the re-testing interval was 2–7 days. Ninety-five patients with positive SARS-CoV-2 nucleic acid test results were included in this study and we retrospectively analysed their CT imaging characteristics.ResultsNinety-five patients underwent 2–3 SARS-CoV-2 nucleic acid tests and received a definitive diagnosis of COVID-19. Fifty-three were male and 42 were female, and their mean age was 42 ± 12 years (range: 10 months to 81 years). Sixty-nine patients (72.6%) experienced fever, fatigue, and dry cough, while 15 (15.8%) had poor appetite and fatigue, and 11 (11.6%) had a dry cough and no fever. On CT imaging, early stage patients (n = 53, 55.8%) showed peripheral subpleural ground-glass opacities; these were mainly local patches (22/53, 41.5%), while some lesions were accompanied by interlobular septal thickening. Thirty-four (35.8%) patients were classified in the ‘progression stage’ based on CT imaging; these patients typically showed lesions in multiple lung segments and lobes (21/34,61.8%), and an uneven increase in ground-glass opacity density accompanied by consolidation and grid-like or cord-like shadows(30.5%). Two patients (2.1%) showed a severe presentation on CT. These showed diffuse bilateral lung lesions, mixed ground-glass opacities and consolidation with cord-like interstitial thickening and air bronchograms, entire lung involvement with a “white lung” presentation, and mild pleural effusion. Six patients in remission (6.3%), visible lesion absorption, fibrotic lesions. Based on clinical signs, 71 (74.7%), 22 (23.2%), and 2 (2.1%) patients had mild or moderate, severe, and critical disease, respectively. Within the follow-up period, 93 patients recovered and were discharged, including the 53 early stage patients and 34 progression stage patients. The length of hospitalisation was 7–28 days (mean: 10 ± 3.5 days). On discharge, lesions were significantly reduced in area and had in many cases completely disappeared, while slight pulmonary fibrosis was present in some patients. One severe stage patient was still hospitalised at the end of the follow-up period and the other severe stage patient died. The overall mortality rate was 1.05%.ConclusionsUnderstanding the CT imaging characteristics of COVID-19 is important for early lesion detection, determining the nature of lesions, and assessing disease severity.     

Chest CT imaging features and clinical outcome of coronavirus disease 2019 (COVID-19): A single-center case study in Ningbo,China

ObjectiveThe purpose of this study was to investigate the chest CT imaging features and clinical outcome of coronavirus disease 2019 (COVID-19) in Ningbo, China.MethodsIn this retrospective study, twenty-eight confirmed and seven highly suspected cases of COVID-19 were enrolled in Ningbo first hospital from January 26, 2020 to March 5, 2020. Cases were confirmed by real-time polymerase chain reaction (RT-PCR). The initial and follow-up chest CT imaging features, epidemiological history, and outcome were analyzed.ResultsThe average age of the patients was 57.3 ± 15.3 years (range: 27–96 years), including 25 females and 10 males. On CT images, 89.3% (25/28) confirmed and 100% (7/7) suspected patients had ground-glass opacities (GGOs), and GGOs with mixed consolidations were observed in 35.7% (10/28) confirmed and 42.9% (3/7) suspected cases, most of these lesions were distributed under the peripheral of both lungs. 17 confirmed and 4 suspected cases had a history of participating in Ningbo Tian-tong Temple rituals and all had GGOs in their lungs during the initial CT scan. As of March 25, 2020, the lung lesions of our cases were significantly resolved and all patients have been discharged from the hospital.ConclusionThe most common chest CT features are multiple bilateral and peripheral GGOs with mixed consolidations or not in the lungs of patients with COVID-19. Chest CT plays an important role in the diagnosis and monitoring treatment response of this disease. There was no reported death in our cases.     

Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters

European Journal of Nuclear Medicine and Molecular Imaging - To quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory...     

Chest CT features and progression of patients with coronavirus disease 2019

Objectives:Coronavirus disease 2019 (COVID-19) is a major public health emergency. It poses a grave threat to human life and health. The purpose of the study is to investigate the chest CT findings and progression of the disease observed in COVID-19 patients.Methods:Forty-nine confirmed cases of adult COVID-19 patients with common type, severe and critically severe type were included in this retrospective single-center study. The thin-section chest CT features and progress of the disease were evaluated. The clinical and chest imaging findings of COVID-19 patients with different severity types were compared. The CT severity score and MuLBSTA score (a prediction of mortality risk) were calculated in those patients.Results:Among the 49 patients, 35 patients (71%) were common type and 14 patients (28%) were severe and critically severe type. Nearly all patients (98%) had pure ground-glass opacities (GGO) in CT imaging. Of the severe and critically severe type patients, 86% exhibited GGO with consolidation, in comparison with 54% of the patients with common type. Fibrosis presented in 79% of the severe and critically severe type patients and 43% of the common type patients. The severe and critically severe type patients were significantly more prone to experience five-lobe involvement compared to the common type patients (p = 0.002). The severe and critically severe type patients also had higher CT severity and MuLBSTA scores than the common type patients (5.43 ± 2.38 vs 3.37 ± 2.40, p < 0.001;and 10.21 ± 3.83 vs 4.63 ± 3.43, p < 0.001, respectively). MuLBSTA score was positively correlated with admittance to the intensive care unit (p = 0.005, r = 0.351). Nineteen patients underwent three times CT scan. The interval between first and second CT scan was 4[4,8] days, second and third was 3[2,4] days. There were greater improvements in the third CT follow-up findings compared to the second (p = 0.002).Conclusions:The severe and critically severe type patients often experienced more severe lung lesions, including GGO with consolidation. The CT severity score and MuLBSTA score may be helpful for the assessment of COVID-19 severity and progression.Advances in knowledge:Chest CT has the value of evaluated radiographical features of COVID-19 and allow for dynamic observation of the disease progression. Considering coagulation disorder of COVID-19, MuLBSTA score may need to be updated to increase new understanding of COVID-19.     

防控新型冠状病毒肺炎的主要做法和体会

根据上级针对新型冠状病毒肺炎疫情防控的统一部署,中国人民解放军总医院第六医学中心党委及全体工作人员能够做到“牢记使命、闻令而动、勇挑重担、敢打硬仗”,通过成立领导小组组织谋划中心防控工作以及通过成立专家组指导临床一线筛查工作;制定中心诊疗方案、门诊和住院收容管理规定、密切接触者管理方案、返京工作人员管理规定、医务人员个人防护要求等方案预案,从制度上约束防控工作;采取“守住前头、看住中心、管住后院”的工作方法,确保防控工作落地见效。疫情防控期间,接诊患者和医务人员“零感染”,发热门诊排查的病历无漏诊情况发生。     

The indispensable role of chest CT in the detection of coronavirus disease 2019 (COVID-19)

European Journal of Nuclear Medicine and Molecular Imaging -     

A review of neuroradiological abnormalities in patients with coronavirus disease 2019 (COVID-19)

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to various neurological manifestations. There is an urgent need for a summary of neuroimaging findings to accelerate diagnosis and treatment plans. We reviewed prospective and retrospective studies to classify neurological abnormalities observed in patients with the SARS-CoV-2 infection.MethodsThe relevant studies published in Scopus, PubMed and Clarivate Analytics databases were analysed. The search was performed for full-text articles published from 23 January 2020 to 23 February 2021.ResultsIn 23 studies the number of patients with SARS-CoV-2 infection was 20,850 and the number of patients with neurological manifestations was 1996 (9.5%). The total number of patients with neuroradiological abnormalities was 602 (2.8%). SARS-CoV-2 has led to various neuroimaging abnormalities which can be categorised by neuroanatomical localisation of lesions and their main probable underlying pathogenesis. Cranial nerve and spinal root abnormalities were cranial neuritis and polyradiculitis. Parenchymal abnormalities fell into four groups of: (a) thrombosis disorders, namely ischaemic stroke and sinus venous thrombosis; (b) endothelial dysfunction and damage disorders manifested as various types of intracranial haemorrhage and posterior reversible encephalopathy syndrome; (c) hypoxia/hypoperfusion disorders of leukoencephalopathy and watershed infarction; and (d) inflammatory disorders encompassing demyelinating disorders, encephalitis, vasculitis-like disorders, vasculopathy and cytotoxic lesions of the corpus callosum. Leptomeninges disorders included meningitis. Ischaemic stroke was the most frequent abnormality in these studies.ConclusionThe review study suggests that an anatomical approach to the classification of heterogeneous neuroimaging findings in patients with SARS-CoV-2 and neurological manifestations would lend itself well for use by practitioners in diagnosis and treatment planning.     

Impact of coronavirus disease 2019 (COVID-19) outbreak on radiology research: An Italian survey

PurposeTo understand how COVID-19 pandemic has changed radiology research in Italy.MethodsA questionnaire (n = 19 questions) was sent to all members of the Italian Society of Radiology two months after the first Italian national lockdown was lifted.ResultsA total of 327 Italian radiologists took part in the survey (mean age: 49 ± 12 years). After national lockdown, the working-flow came back to normal in the vast majority of cases (285/327, 87.2%). Participants reported that a total of 462 radiological trials were recruiting patients at their institutions prior to COVID-19 outbreak, of which 332 (71.9%) were stopped during the emergency. On the other hand, 252 radiological trials have been started during the pandemic, of which 156 were non-COVID-19 trials (61.9%) and 96 were focused on COVID-19 patients (38.2%). The majority of radiologists surveyed (61.5%) do not conduct research. Of the radiologists who carried on research activities, participants reported a significant increase of the number of hours per week spent for research purposes during national lockdown (mean 4.5 ± 8.9 h during lockdown vs. 3.3 ± 6.8 h before lockdown; p = .046), followed by a significant drop after the lockdown was lifted (3.2 ± 6.5 h per week, p = .035). During national lockdown, 15.6% of participants started new review articles and completed old papers, 14.1% completed old works, and 8.9% started new review articles. Ninety-six surveyed radiologists (29.3%) declared to have submitted at least one article during COVID-19 emergency.ConclusionThis study shows the need to support radiology research in challenging scenarios like COVID-19 emergency.     

Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging

PurposeTo evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity.MethodsWe conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals.ResultsMedian VR max was 18.6% (IQR 9.1–32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4–5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4–5.5%) in severe and 0.4% (IQR 0.1–0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%.ConclusionFive days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.     

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions

Objectives:To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).Methods:Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.Results:A total of 107 patients (median age, 49.0 years, interquartile range, 35–54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3–5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766–0.947), sensitivity of 87.5%, and specificity of 70.7%.Conclusions:Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.Advances in knowledge:Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.     

Key elements of preparedness for pandemic coronavirus disease 2019 (COVID-19) in nuclear medicine units

European Journal of Nuclear Medicine and Molecular Imaging -     

Magnetic resonance imaging features of coronavirus disease 2019 (COVID-19) pneumonia: The first preliminary case series

Rationale and objectivesThere is a rising onus on understanding the common features of COVID-19 pneumonia on different imaging modalities. In this study, we aimed to review and depict the common MRI features of COVID-19 pneumonia in our laboratory confirmed case series, the first comprehensive reported cohort in the literature.Materials and methodsUpon IRB approval, eight laboratory confirmed COVID-19 patients who presented to our outpatient imaging clinic underwent chest CT and, once various features of COVID-19 pneumonia were identified, a dedicated multisequence chest MRI was performed on the same day with an institutional protocol. Demographic data and the morphology, laterality and location of the lesions were recorded for each case.ResultsFive males and three females with the mean age of 40.63 ± 12.64 years old were present in this case series. Five cases had typical CT features with ground glass opacities and consolidations, readily visible on different MRI sequences. Three cases had indeterminate or atypical features which were also easily seen on MRI. The comprehensive review of MRI features for each case and representative images have been illustrated.ConclusionBecoming familiar with typical findings of COVID-19 pneumonia in MRI is crucial for every radiologist. Although MRI is not the modality of choice for evaluation of pulmonary opacities, it has similar capabilities in detection of COVID-19 pneumonia when compared to chest CT.     

Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis

PurposeTo date, considerable knowledge gaps remain regarding the chest CT imaging features of coronavirus disease 2019 (COVID-19). We performed a systematic review and meta-analysis of results from published studies to date to provide a summary of evidence on detection of COVID-19 by chest CT and the expected CT imaging manifestations.MethodsStudies were identified by searching PubMed database for articles published between December 2019 and February 2020. Pooled CT positive rate of COVID-19 and pooled incidence of CT imaging findings were estimated using a random-effect model.ResultsA total of 13 studies met inclusion criteria. The pooled positive rate of the CT imaging was 89.76% and 90.35% when only including thin-section chest CT. Typical CT signs were ground glass opacities (83.31%), ground glass opacities with mixed consolidation (58.42%), adjacent pleura thickening (52.46%), interlobular septal thickening (48.46%), and air bronchograms (46.46%). Other CT signs included crazy paving pattern (14.81%), pleural effusion (5.88%), bronchiectasis (5.42%), pericardial effusion (4.55%), and lymphadenopathy (3.38%). The most anatomic distributions were bilateral lung infection (78.2%) and peripheral distribution (76.95%). The incidences were highest in the right lower lobe (87.21%), left lower lobe (81.41%), and bilateral lower lobes (65.22%). The right upper lobe (65.22%), right middle lobe (54.95%), and left upper lobe (69.43%) were also commonly involved. The incidence of bilateral upper lobes was 60.87%. A considerable proportion of patients had three or more lobes involved (70.81%).ConclusionsThe detection of COVID-19 chest CT imaging is very high among symptomatic individuals at high risk, especially using thin-section chest CT. The most common CT features in patients affected by COVID-19 included ground glass opacities and consolidation involving the bilateral lungs in a peripheral distribution.     

Coronavirus Disease 2019 (COVID-19) Pneumonia Presentations in Chest Computed Tomography: A Pictorial Review

Despite imaging not being a tool for novel coronavirus disease 2019 (COVID-19) diagnosis, there has been an increased number of chest computed tomography (CT) scans done worldwide. There are no pathognomonic CT features for COVID-19 pneumonia, as findings are also common in other infectious diseases and noninfectious aetiologies. Nonetheless, point-of-care physicians should be familiarized with the most common imaging presentations of the COVID-19. In this pictorial review, we have summarized the most reported imaging features of COVID-19 pneumonia, including possible differential diagnosis according to the CT finding.