Chest computed tomography as a primary tool in COVID-19 detection: an update meta-analysis.
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ABSTRACT: Purpose
A growing number of publications have paid close attention to the chest computed tomography (CT) detection of COVID-19 with inconsistent diagnostic accuracy, the present meta-analysis assessed the available evidence regarding the overall performance of chest CT for COVID-19.Methods
2 × 2 diagnostic table was extracted from each of the included studies. Data on specificity (SPE), sensitivity (SEN), negative likelihood ratio (LR-), positive likelihood ratio (LR+), and diagnostic odds ratio (DOR) were calculated purposefully.Results
Fifteen COVID-19 related publications met our inclusion criteria and were judged qualified for the meta-analysis. The following were summary estimates for diagnostic parameters of chest CT for COVID-19: SPE, 0.49 (95% CI 46-52%); SEN, 0.94 (95% CI 93-95%); LR-, 0.15 (95% CI 11-20%); LR+, 1.93 (95% CI 145-256%); DOR, 17.14 (95% CI 918-3199%); and the area under the receiver operating characteristic curve (AUC), 0.93.Conclusion
Chest CT has high SEN, but the SPE is not ideal. It is highly recommended to use a combination of different diagnostic tools to achieve sufficient SEN and SPE. It should be taken into account as a diagnostic tool for current COVID-19 detection, especially for patients with symptoms.Supplementary information
The online version contains supplementary material available at 10.1007/s40336-021-00434-z.
SUBMITTER: Pang C
PROVIDER: S-EPMC8149579 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivePatients with coronavirus disease 2019 (COVID-19) pneumonia present with typical findings on chest computed tomography (CT), but the underlying histopathological patterns are unknown. Through direct regional correlation of imaging findings to histopathological patterns, this study aimed to explain typical COVID-19 CT patterns at tissue level.MethodsEight autopsy cases were prospectively selected of patients with PCR-proven COVID-19 pneumonia with varying clinical manifestations and causes of death. All had been subjected to chest CT imaging 24-72 h prior to death. Twenty-seven lung areas with typical COVID-19 patterns and two radiologically unaffected pulmonary areas were correlated to histopathological findings in the same lung regions.ResultsTwo dominant radiological patterns were observed: ground-glass opacity (GGO) (n = 11) and consolidation (n = 16). In seven of 11 sampled areas of GGO, diffuse alveolar damage (DAD) was observed. In four areas of GGO, the histological pattern was vascular damage and thrombosis, with (n = 2) or without DAD (n = 2). DAD was also observed in five of 16 samples derived from areas of radiological consolidation. Seven areas of consolidation were based on a combination of DAD, vascular damage and thrombosis. In four areas of consolidation, bronchopneumonia was found. Unexpectedly, in samples from radiologically unaffected lung parenchyma, evidence was found of vascular damage and thrombosis.ConclusionIn COVID-19, radiological findings of GGO and consolidation are mostly explained by DAD or a combination of DAD and vascular damage plus thrombosis. However, the different typical CT patterns in COVID-19 are not related to specific histopathological patterns. Microvascular damage and thrombosis are even encountered in the radiologically normal lung.
| S-EPMC8447040 | biostudies-literature
Project description:Purpose:Real-time polymerase chain reaction (RT-PCR) and chest computed tomography (CT) scan are main diagnostic modalities of coronavirus disease 2019 (COVID-19). However, there is still no consensus on which of these methods is superior to the other. Therefore, the present meta-analysis was designed to answer to the question whether CT scan can be used in diagnosis of COVID-19 or not. Methods:Searches were performed in Medline, Embase, Scopus, and Web of Science databases until the end of April 2020. Two researchers gathered the data of diagnostic accuracy studies that had attempted to evaluate sensitivity and specificity of CT scan in diagnosis of COVID-19. Results:Data of 9 studies were included. Area under the curve of ground glass opacity (GGO), consolidation, pleural effusion, other CT features, and simultaneous observation of GGO with other CT features was 0.64 (95% CI 0.60-0.69), 0.30 (95% CI 0.26-0.34), 0.60 (95% CI 0.56-0.64), 0.61 (95% CI 0.56-0.65), and 0.90 (95% CI 0.87-0.92), respectively. Sensitivity and specificity of simultaneous observation of GGO with other CT scan features was higher than all of the other signs. Sensitivity, specificity, and diagnostic odds ratio of this sign was calculated to be 0.90, 0.89, and 20, respectively. Conclusion:Simultaneous observation of GGO and other features of viral pneumonia in CT scan had optimum performance in detection of COVID-19. However, it is suggested to make the final diagnosis based on both CT scan and RT-PCR, as none of the two diagnostic modalities are reliable alone.
| S-EPMC7549426 | biostudies-literature
Project description:Objective:The COVID-19 pandemic and annual influenza epidemic are responsible for thousands of deaths globally. With a similarity in clinical as well as laboratory findings, there is a need to differentiate these two conditions on chest CT scan. This paper attempts to use existing literature to draw out differences in chest CT findings in COVID-19 and influenza. Methods:A search was conducted using PubMed. 17 original studies on chest CT findings in COVID-19 and influenza were identified for full-text review and data analysis. Findings. COVID-19 and influenza share similar chest CT findings. The differences found show that COVID-19 ground-glass opacities are usually peripherally located with the lower lobes being commonly involved, while influenza has a central, peripheral, or random distribution usually affecting the five lobes. Vascular engorgement, pleural thickening, and subpleural lines were reported in COVID-19 patients. In contrast, pneumomediastinum and pneumothorax were reported only in studies on influenza. Conclusion and Relevance. COVID-19 and influenza have overlapping chest CT features with few differences which can assist in telling apart the two pathologies. Additional studies are needed to further define the differences and degree between COVID-19 and influenza.
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Project description:Introduction: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.Methods: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated.Results: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p?>?0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p?<?0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates.Conclusion: Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.
| S-EPMC7641539 | biostudies-literature
Project description:Available information on chest Computed Tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) is constantly evolving. Ground glass opacities and consolidation with bilateral and peripheral distribution were reported as the most common CT findings, but also less typical features could be identified. All radiologists should be aware of the imaging spectrum of the COVID-19 pneumonia and imaging changes in the course of the disease. Our aim is to display the chest CT findings at first assessment and follow-up through a pictorial essay, to help in the recognition of these features for an accurate diagnosis.
| S-EPMC7315402 | biostudies-literature
Project description:The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system, which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the chest CT scans which shows the regions of infection in the lungs through an algorithm developed as a part of this work, and verified through radiologists. We propose a novel selection approach combined with multi-threading for a faster generation of heatmaps on a Mobile Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with a F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high-volume practices and help doctors triage patients for the early diagnosis of COVID-19 quickly and efficiently.
| S-EPMC8858433 | biostudies-literature
Project description:ObjectiveThe aim of our study was to assess the effect of a short-term treatment with low-moderate corticosteroid (CS) doses by both a quantitative and qualitative assessment of chest HRCT of COVID-19 pneumonia.MethodsCORTICOVID is a single-center, cross-sectional, retrospective study involving severe/critical COVID-19 patients with mild/moderate ARDS. Lung total severity score was obtained according to Chung and colleagues. Moreover, the relative percentages of lung total severity score by ground glass opacities, consolidations, crazy paving, and linear bands were computed. Chest HRCT scores, P/F ratio, and laboratory parameters were evaluated before (pre-CS) and 7-10 days after (post-CS) methylprednisolone of 0.5-0.8 mg/kg/day.FindingsA total of 34 severe/critical COVID-19 patients were included in the study, of which 17 received Standard of Care (SoC) and 17 CS therapy in add-on. CS treatment disclosed a significant decrease in HRCT total severity score [median = 6 (IQR: 5-7.5) versus 10 (IQR: 9-13) in SoC, p < 0.001], as well in single consolidations [median = 0.33 (IQR: 0-0.92) versus 6.73 (IQR: 2.49-8.03) in SoC, p < 0.001] and crazy paving scores [mean = 0.19 (SD = 0.53) versus 1.79 (SD = 2.71) in SoC, p = 0.010], along with a significant increase in linear bands [mean = 2.56 (SD = 1.65) versus 0.97 (SD = 1.30) in SoC, p = 0.006]. GGO score instead did not significantly differ at the end of treatment between the two groups. Most post-CS GGO, however, derived from previous consolidations and crazy paving [median = 1.5 (0.35-3.81) versus 2 (1.25-3.8) pre-CS; p = 0.579], while pre-CS GGO significantly decreased after methylprednisolone therapy [median = 0.66 (0.05-1.33) versus 1.5 (0.35-3.81) pre-CS; p = 0.004]. CS therapy further determined a significant improvement in P/F levels [median P/F = 310 (IQR: 235.5-370) versus 136 (IQR: 98.5-211.75) in SoC; p < 0.001], and a significant increase in white blood cells, lymphocytes, and neutrophils absolute values.ConclusionThe improvement of all chest HRCT findings further supports the role of CS adjunctive therapy in severe/critical COVID-19 pneumonia.
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Project description:Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
| S-EPMC9356575 | biostudies-literature
Project description:PurposeThe novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.Material and methodsHerein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.ResultsAll proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.ConclusionsOur data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.
| S-EPMC9453472 | biostudies-literature
Project description:BackgroundThe outbreak of the coronavirus disease 2019 (COVID-19) has had a massive impact on the whole world. Computed tomography (CT) has been widely used in the diagnosis of this novel pneumonia. This study aims to understand the role of CT for the diagnosis and the main imaging manifestations of patients with COVID-19.MethodsWe conducted a rapid review and meta-analysis on studies about the use of chest CT for the diagnosis of COVID-19. We comprehensively searched databases and preprint servers on chest CT for patients with COVID-19 between 1 January 2020 and 31 March 2020. The primary outcome was the sensitivity of chest CT imaging. We also conducted subgroup analyses and evaluated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.ResultsA total of 103 studies with 5,673 patients were included. Using reverse transcription polymerase chain reaction (RT-PCR) results as reference, a meta-analysis based on 64 studies estimated the sensitivity of chest CT imaging in COVID-19 was 99% (95% CI, 0.97-1.00). If case reports were excluded, the sensitivity in case series was 96% (95% CI, 0.93-0.99). The sensitivity of CT scan in confirmed patients under 18 years old was only 66% (95% CI, 0.15-1.00). The most common imaging manifestation was ground-glass opacities (GGO) which was found in 75% (95% CI, 0.68-0.82) of the patients. The pooled probability of bilateral involvement was 84% (95% CI, 0.81-0.88). The most commonly involved lobes were the right lower lobe (84%, 95% CI, 0.78-0.90) and left lower lobe (81%, 95% CI, 0.74-0.87). The quality of evidence was low across all outcomes.ConclusionsIn conclusion, this meta-analysis indicated that chest CT scan had a high sensitivity in diagnosis of patients with COVID-19. Therefore, CT can potentially be used to assist in the diagnosis of COVID-19.
| S-EPMC7290647 | biostudies-literature