Chest CT abnormalities in COVID-19: a systematic review.
Ontology highlight
ABSTRACT:
SUBMITTER: Ghayda RA
PROVIDER: S-EPMC8436104 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:OBJECTIVE:The objective of this systematic review was to evaluate the key imaging manifestations of COVID-19 on chest CT in adult patients by providing a comprehensive review of the published literature. METHODS:We performed a systematic literature search from the PubMed, Google Scholar, Embase, and WHO databases for studies mentioning the chest CT imaging findings of adult COVID-19 patients. RESULTS:A total of 45 studies comprising 4410 patients were included. Ground glass opacities (GGO), in isolation (50.2%) or coexisting with consolidations (44.2%), were the most common lesions. Distribution of GGOs was most commonly bilateral, peripheral/subpleural, and posterior with predilection for lower lobes. Common ancillary findings included pulmonary vascular enlargement (64%), intralobular septal thickening (60%), adjacent pleural thickening (41.7%), air bronchograms (41.2%), subpleural lines, crazy paving, bronchus distortion, bronchiectasis, and interlobular septal thickening. CT in early follow-up period generally showed an increase in size, number, and density of GGOs, with progression into mixed areas of GGOs plus consolidations and crazy paving, peaking at 10-11 days, before gradually resolving or persisting as patchy fibrosis. While younger adults more commonly had GGOs, extensive/multilobar involvement with consolidations was prevalent in the older population and those with severe disease. CONCLUSION:This review describes the imaging features for diagnosis, stratification, and follow-up of COVID-19 patients. The most common CT manifestations are bilateral, peripheral/subpleural, posterior GGOs with or without consolidations with a lower lobe predominance. It is pertinent to be familiar with the various imaging findings to positively impact the management of these patients. KEY POINTS:• Ground glass opacities (GGOs), whether isolated or coexisting with consolidations, in bilateral and subpleural distribution, are the most prevalent chest CT findings in adult COVID-19 patients. • Follow-up CT shows a progression of GGOs into a mixed pattern, reaching a peak at 10-11 days, before gradually resolving or persisting as patchy fibrosis. • Younger people tend to have more GGOs. Older or sicker people tend to have more extensive involvement with consolidations.
| S-EPMC7261039 | biostudies-literature
Project description:AimTo determine the overall rate of chest imaging findings in asymptomatic cases, describe the most common patterns found, and determine the rate of later symptom development in these initially asymptomatic cases.Materials and methodsThe PubMed and EMBASE databases were searched until 1 May 2020, for studies examining the proportion of positive chest imaging findings in asymptomatic cases diagnosed with COVID-19 and a random-effects meta-analysis of proportions was performed. Heterogeneity was assessed using the I2 statistic.ResultsAmong 858 non-duplicate studies, seven studies with a total of 231 asymptomatic cases met the inclusion criteria. In the primary analysis, the pooled estimate of the overall rate of positive chest computed tomography (CT) findings among asymptomatic cases was 63% (95% confidence interval [CI]: 44-78%). Among 155/231 cases that were followed up for later symptom development, 90/155 remained asymptomatic and 65/155 developed symptoms during the study period (that ranged between seven and 30 days of follow-up). The pooled estimate of the rate of positive chest CT findings was 62% (95% CI: 38-81%) in cases that remained asymptomatic, while it was 90% (95% CI: 49-99%) in cases that developed symptoms. Among CT findings, the pooled estimate of the overall rate of ground-glass opacities (GGO) at CT alone was 71% (95% CI: 50-86%). Among other CT findings reported, 22/231 patients had GGO with consolidation, 7/231 patients had stripe shadows with or without GGO, and 8/231 patients had GGO with interlobular septal thickening. Among initially asymptomatic cases with positive CT findings, the pooled estimate of the overall rate of later symptom development was 26% (95% CI: 14-43%).ConclusionIn COVID-19, asymptomatic cases can have positive chest CT findings, and COVID-19 should be considered among cases with CT abnormalities even when there are no other symptoms. There is a need for close clinical monitoring of asymptomatic cases with radiographic findings as a significant percentage will develop symptoms.
| S-EPMC7420957 | biostudies-literature
Project description:While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1-6 months) and long-term (12-24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3-0.59]) and long-term (0.38 [0.23-0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (β = -0.137, p = 0.002), ground-glass opacities (β = -0.169, p < 0.001), increased for honeycombing (β = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.
| S-EPMC10969005 | biostudies-literature
Project description:The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
| S-EPMC10486542 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, has rapidly swept around the world just within a month, causing global public health emergency. In diagnosis, chest computed tomography (CT) manifestations can supplement parts of limitations of real-time reverse transcription polymerase chain reaction (RT-PCR) assay. Based on a comprehensive literature review and the experience in the frontline, we aim to review the typical and relatively atypical CT manifestations with representative COVID-19 cases at our hospital, and hope to strengthen the recognition of these features with radiologists and help them make a quick and accurate diagnosis.Key Points• Ground glass opacities, consolidation, reticular pattern, and crazy paving pattern are typical CT manifestations of COVID-19.• Emerging atypical CT manifestations, including airway changes, pleural changes, fibrosis, nodules, etc., were demonstrated in COVID-19 patients.• CT manifestations may associate with the progression and prognosis of COVID-19.
| S-EPMC7088323 | biostudies-literature
Project description:The objective of this systematic review is to give a comprehensive interpretation of menstrual cycle changes after the COVID-19 vaccination. Additionally, it is imperative to assess reports of menstrual changes following vaccination to dispel concerns that COVID-19 vaccines hinder the likelihood of pregnancy in the long run. A literature review was conducted using digital databases to systematically identify the studies reporting any menstrual abnormalities after the COVID-19 vaccine. Detailed patient-level study characteristics including the type of study, sample size, administered vaccines, and menstrual abnormalities were abstracted. A total of 78 138 vaccinated females were included in this review from 14 studies. Of these, 39 759 (52.05%) had some form of a menstrual problem after vaccination. Due to the lack of published research articles, preprints were also included in this review. Menorrhagia, metrorrhagia, and polymenorrhea were the most commonly observed problems and the overall study-level rate of menstrual abnormality ranged from 0.83% to 90.9%. Age, history of pregnancy, systemic side-effects of COVID-19, smoking, and second dose of COVID-19 vaccine were predictors of menstrual problems after vaccination.
| S-EPMC9294036 | biostudies-literature
Project description:ObjectivesTo compare the performance of chest computed tomography (CT) scan versus reverse transcription polymerase chain reaction (RT-PCR) as the reference standard in the initial diagnostic assessment of coronavirus disease 2019 (COVID-19) patients.DesignA systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A search of electronic information was conducted using the following databases: MEDLINE, EMBASE, EMCARE, CINAHL and the Cochrane Central Register of Controlled Trials.SettingStudies that compared the diagnostic performance within the same patient cohort of chest CT scan versus RT-PCR in COVID-19 suspected patients.ParticipantsThirteen non-randomised studies enrolling 4092 patients were identified.Main outcome measuresSensitivity, specificity and accuracy were primary outcome measures. Secondary outcomes included other test performance characteristics and discrepant findings between both investigations.ResultsChest CT had a median sensitivity, specificity and accuracy of 0.91 (range 0.82-0.98), 0.775 (0.25-1.00) and 0.87 (0.68-0.99), respectively, with RT-PCR as the reference. Importantly, early small, China-based studies tended to favour chest CT versus later larger, non-China studies.ConclusionsA relatively high false positive rate can be expected with chest CT. It is possible it may still be useful to provide circumstantial evidence, however, in some patients with a suspicious clinical presentation of COVID-19 and negative initial Severe Acute Respiratory Syndrome Coronavirus 2 RT-PCR tests, but more evidence is required in this context. In acute cardiorespiratory presentations, negative CT scan and RT-PCR tests is likely to be reassuring.
| S-EPMC8127597 | biostudies-literature
Project description:Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45-100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93-100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96-74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99-9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67-100%), consolidation (44.18%, 95% CI: 1.61-71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05-76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78-80.39%), linear opacities (41.29%, 95% CI: 7.44-65.06%), crazy-paving pattern (23.57%, 95% CI: 3.13-91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90-80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74-38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.
| S-EPMC7242306 | biostudies-literature
Project description:Background and aimsThis study aimed to evaluate the severity of diagnosed lung abnormalities of coronavirus disease 2019 (COVID-19) patients based on the pre-and postrecovery follow-up chest computed tomography (CT) scan findings done at regular intervals.MethodsThis cross-sectional study was performed in three phases. The severity of lung abnormalities was recorded and compared based on the initial and follow-up chest CT findings carried out pre-and at regular intervals (3 and 6 months) of postrecovery of COVID-19 patients. Statistical data analysis was conducted using SPSS-Version 26. Pearson Chi-square test was used to analyze the results. p-value < 0.05 was considered statistically significant.ResultsRegarding the initial chest CT findings, although ground-glass opacity (GGO) was observed as the most common lung lesion, almost all the evaluated COVID-19 patients had multiple lung lesions and involvements, especially with more involvement of the lower lobes. concerning the frequency of lung lesions and involvements in all phases of the study, almost no statistically significant differences were observed between male and female COVID-19 patients and different age groups. However, older age groups had relatively more lung abnormalities due to Covid-19 based on initial CT images which take more time to be eliminated. Lung abnormalities of Covid-19 patients decreased significantly during the follow ups based on chest CT findings at different study phases.ConclusionAccording to evaluated pre- and post-recovery chest CT scans, the frequency of lung lesions and lung involvement distribution decreased significantly in COVID-19 patients, 3 and 6 months after recovery, and most of the recovered patients had no lung lesions or involvement anymore.
| S-EPMC9466357 | biostudies-literature
Project description:Background Some coronavirus disease 2019 (COVID-19) survivors experience prolonged and varying symptoms, a condition termed post-acute COVID-19 syndrome (PACS). However, the prevalence of chronic pulmonary sequelae of PACS during long-term follow-up remains unclear. Several studies have examined this issue and reported heterogeneous results. Methods We conducted a systematic review and meta-analysis using a random-effects model to estimate the pooled prevalence of the pulmonary sequelae of COVID-19, as demonstrated by pulmonary function testing (PFT) and chest computed tomography (CT) performed at least 6 months after initial infection. PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020 to December 31, 2021 to identify related studies. We investigated whether the prevalence of pulmonary sequelae decreased over time and attempted to identify the factors associated with their development by performing multiple subgroup and meta-regression analyses. Results Of the 18,062 studies identified, 30 met our eligibility criteria. Among these studies, 25 and 22 had follow-up PFT and chest CT data, respectively. The follow-up durations were approximately 6 and 12 months in 18 and 12 studies, respectively. Impaired diffusion capacity was the most common abnormality on PFT (pooled prevalence 35%, 95% confidence interval [CI] 30–41%) with a prevalence of 39% (95% CI 34–45%) and 31% (95% CI 21–40%) in the 6-month and 12-month follow-up studies, respectively (P = 0.115). Restrictive pulmonary dysfunction evident as reduced forced vital capacity was less frequent (pooled prevalence 8%, 95% CI 6–11%); however, its prevalence was lower in the 12-month follow-up studies than in the 6-month follow-up studies (5% [95% CI 3–7%] vs. 13% [95% CI 8–19%], P = 0.006). On follow-up chest CT, the pooled prevalence of persistent ground-glass opacities and pulmonary fibrosis was 34% (95% CI 24–44%) and 32% (95% CI 23–40%), respectively, and the prevalence did not decrease over time. As every meta-analysis showed significant between-study heterogeneity, subgroup and meta-regression analyses were performed to identify potential effect modifiers; the severity of index infection was associated with the prevalence of impaired diffusion capacity and pulmonary fibrosis. Conclusions A substantial number of COVID-19 survivors displayed pulmonary sequelae as part of PACS. Except for restrictive pulmonary dysfunction, the prevalence of these sequelae did not decrease until 1 year after initial infection. Considering the association between the severity of acute COVID-19 and risk of pulmonary sequelae, patients who recover from severe COVID-19 require close respiratory follow-up. Systematic review registration number PROSPERO CRD42021234357 Supplementary Information The online version contains supplementary material available at 10.1186/s12931-022-02163-x.
| S-EPMC9446643 | biostudies-literature