Dataset Information

Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists.

ABSTRACT: COVID-19 is an emerging infection caused by a novel coronavirus that is moving so rapidly that on 30 January 2020 the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on 11 March 2020 as a pandemic. An early diagnosis of COVID-19 is crucial for disease treatment and control of the disease spread. Real-time reverse-transcription polymerase chain reaction (RT-PCR) demonstrated a low sensibility; therefore chest computed tomography (CT) plays a pivotal role not only in the early detection and diagnosis, especially for false negative RT-PCR tests, but also in monitoring the clinical course and in evaluating the disease severity. This paper reports the CT findings with some hints on the temporal changes over the course of the disease: the CT hallmarks of COVID-19 are bilateral distribution of ground glass opacities with or without consolidation in the posterior and peripheral lung, but the predominant findings in later phases include consolidations, linear opacities, "crazy-paving" pattern, "reversed halo" sign and vascular enlargement. The CT findings of COVID-19 overlap with the CT findings of other diseases, in particular the viral pneumonia including influenza viruses, parainfluenza virus, adenovirus, respiratory syncytial virus, rhinovirus, human metapneumovirus, etc. There are differences as well as similarities in the CT features of COVID-19 compared with those of the severe acute respiratory syndrome. The aim of this article is to review the typical and atypical CT findings in COVID-19 patients in order to help radiologists and clinicians to become more familiar with the disease.

SUBMITTER: Carotti M

PROVIDER: S-EPMC7270744 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists.

Carotti Marina M Salaffi Fausto F Sarzi-Puttini Piercarlo P Agostini Andrea A Borgheresi Alessandra A Minorati Davide D Galli Massimo M Marotto Daniela D Giovagnoni Andrea A

La Radiologia medica 20200604 7

COVID-19 is an emerging infection caused by a novel coronavirus that is moving so rapidly that on 30 January 2020 the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on 11 March 2020 as a pandemic. An early diagnosis of COVID-19 is crucial for disease treatment and control of the disease spread. Real-time reverse-transcription polymerase chain reaction (RT-PCR) demonstrated a low sensibility; therefore chest computed tomography (CT) plays a ...[more]

PMID: 32500509

Similar Datasets

Project description:INTRODUCTION:The evolution of computed tomography (CT) findings in patients with mild coronavirus disease 2019 (COVID-19) pneumonia has not been described in detail. A large-scale longitudinal study is urgently required. METHODS:We analyzed 606 CT scans of 182 patients. The dynamic evolution of CT scores was evaluated using two staging methods: one was divided into 10 periods based on decile intervals, and the other was one stage per week. Moreover, the latter was used to evaluate the dynamic evolution of imaging performance. A published severity scoring system was used to compare findings of the two methods. RESULTS:In the dynamic evolution of 10 stages, the total lesion CT score peaked during stage 3 (9-11 days) and stage 6 (17-18 days), with scores?=?7.19?±?3.66 and 8.00?±?4.57, respectively. The consolidation score peaked during stage 6 (17-18 days; score?=?2.72?±?3.07). In contrast, when a 1-week interval was used and time was divided into five stages, the total lesion score peaked during week 3 (score?=?7.3?±?4.15). The consolidation score peaked during week 2 (score?=?2.54?±?3.25). The predominant CT patterns differed significantly during each stage (P?<?0.01). Ground-glass opacities (GGO), with an increased trend during week 3 and beyond, was the most common pattern in each stage (33-46%). The second most common patterns during week 1 were GGO and consolidation (24%). The linear opacity pattern with an increased trend was the second most common pattern during week 2 and beyond (21-32%). CONCLUSIONS:The total lesion score of mild COVID-19 pneumonia peaked 17-18 days after disease onset. The consolidation scores objectively reflected the severity of the lung involvement compared with total lesion scores. Each temporal stage of mild COVID-19 pneumonia mainly manifested as GGO pattern. Moreover, good prognosis may be associated with increases in the proportions of the GGO and linear opacity patterns during the later stage of disease.

Project description:ObjectivesTo utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.MethodsA deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.ResultsOf 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.ConclusionsThe algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.Key points• The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

Project description:Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Project description:ObjectiveAimed to summarize the characteristics of chest CT imaging in Chinese hospitalized patients with Coronavirus Disease 2019 (COVID-19) to provide reliable evidence for further guiding clinical routine.MethodsPubMed, Embase and Web of Science databases were searched to identify relevant articles involving the features of chest CT imaging in Chinese patients with COVID-19. All data were analyzed utilizing R i386 4.0.0 software. Random-effects models were employed to calculate pooled mean differences.Results19 retrospective studies (1332 cases) were included. The results demonstrated that the combined proportion of ground-glass opacities (GGO) was 0.79 (95% CI 0.68, 0.89), consolidation was 0.34 (95% CI 0.23, 0.47); mixed GGO and consolidation was 0.46 (95% CI 0.37; 0.56); air bronchogram sign was 0.41 (95% CI 0.26; 0.55); crazy paving pattern was 0.32 (95% CI 0.17, 0.47); interlobular septal thickening was 0.55 (95% CI 0.42, 0.67); reticulation was 0.30 (95% CI 0.12, 0.48); bronchial wall thickening was 0.24 (95% CI 0.11, 0.40); vascular enlargement was 0.74 (95% CI 0.64, 0.86); subpleural linear opacity was 0.28 (95% CI 0.12, 0.48); intrathoracic lymph node enlargement was 0.03 (95% CI 0.00, 0.07); pleural effusions was 0.03 (95% CI 0.02, 0.06). The distribution in lung: the combined proportion of central was 0.05 (95% CI 0.01, 0.11); peripheral was 0.74 (95% CI 0.62, 0.84); peripheral involving central was 0.38 (95% CI 0.19, 0.75); diffuse was 0.19 (95% CI 0.06, 0.32); unifocal involvement was 0.09 (95% CI 0.05, 0.14); multifocal involvement was 0.57 (95% CI 0.48, 0.68); unilateral was 0.16 (95% CI 0.10, 0.23); bilateral was 0.83 (95% CI 0.78, 0.89); The combined proportion of lobes involved (> 2) was 0.70 (95% CI 0.61, 0.78); lobes involved (≦ 2) was 0.35 (95% CI 0.26, 0.44).ConclusionGGO, vascular enlargement, interlobular septal thickening more frequently occurred in patients with COVID-19, which distribution features were peripheral, bilateral, involved lobes > 2. Therefore, based on chest CT features of COVID-19 mentioned, it might be a promising means for identifying COVID-19.

Project description:ObjectivesThe aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features.Materials and methodsStudy sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed.ResultsTotally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05).ConclusionsThe common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.

Dataset Information

Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists.

Publications

Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets