Mimickers of novel coronavirus disease 2019 (COVID-19) on chest CT: spectrum of CT and clinical features

Elmokadem, Ali H.; Batouty, Nihal M.; Bayoumi, Dalia; Gadelhak, Basma; Abdel-Wahab, Rihame M.; Zaky, Mona; Abo-Hedibah, Sherif A.; Ehab, Ahmed; El-Morsy, Ahmed

doi:10.1186/s13244-020-00956-6

Cited by 14 publications

(9 citation statements)

References 54 publications

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“…Thus, it is unlikely that imaging will confirm the diagnosis or exclude the possibility of infection with high confidence if imaging is normal, CT should not be used a screening tool in suspected asymptomatic cases for the cost of radiation. There is an overlap between the imaging presentation of COVID-19 and other pulmonary conditions such as viral pneumonias, atypical bacterial pneumonia, or hypersensitivity and eosinophilic pneumonias, which can lead to false-positive results [ 67 - 69 ]. In our studies there was a lack of data about the specificity of the CT as regards other pulmonary infections.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients

Ghoneim¹,

Eid²,

Hamdy³

et al. 2022

Pol J Radiol

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Purpose The outbreak of a new coronavirus is still spreading worldwide, affecting children and adults. However, COVID-19 in children shows distinctive characteristics in clinical and radiological presentation. We aimed to assess the diagnostic performance of chest CT and clarify the clinicoradiological CT features of COVID-19 among children with COVID-19. Material and methods Adhering to PRISMA-DTA guidelines, we searched databases (PubMed, Google Scholar, and Web of Science) to identify relevant articles. The search keywords were: “Chest CT” AND “COVID-19” OR “coronavirus” OR “SARS-COV-2” AND “Children” OR “Pediatric”. Published reports providing clinical and imaging findings of paediatric COVID-19 were included. Results Twenty-eight studies were included, with 987 patients. Most of the patients were symptomatic (76.9%; 95% CI: 69.2-84.7%), with fever being the most frequent manifestation (64%; 95% CI: 58.0-71.2%). Only 2.3% of the cases were critical, and mortality was reported in one case. The proportion of COVID-19 detected by chest CT among children is relatively high (658/987), with ground-glass opacity (GGO) being the most prevalent feature (52.5%; 95% CI: 40.5-64.7%). The pooled sensitivity of chest CT in all patients was 67%; however, it was different between symptomatic and asymptomatic patients (71% and 33%, respectively). The pooled specificity was (67%), which was calculated after considering the symptomatic PCR-positive patients as the gold standard. Conclusions Chest CT showed moderate pooled sensitivity and specificity among symptomatic children with COVID-19 and low sensitivity among asymptomatic children. This means that CT is not to be used as a screening tool or for confirmation of the diagnosis in children and should be reserved for specific clinical situations.

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Section: Discussionmentioning

confidence: 99%

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients

Ghoneim¹,

Eid²,

Hamdy³

et al. 2022

Pol J Radiol

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“…CT chest plays an imperative role in screening, diagnosing, and evaluating the course of COVID-19 and selecting the appropriate management [17,18]. Although chest CT has high sensitivity in COVID diagnosis, it has low specificity as it could be challenging to discriminate COVID-19 from other viral diseases on chest CT [18][19][20]. The chest CT abnormalities during COVID-19 are variable, and the most common changes are multifocal ground-glass opacities with or without consolidation with favorable peripheral distribution [4,9,19,21,22], including ground-glass opacities, consolidation, linear opacities, a crazy-paving pattern, and bronchial wall thickening.…”

Section: Introductionmentioning

confidence: 99%

Comparison of chest CT severity scoring systems for COVID-19

et al. 2022

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Purpose To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time. Methods and materials This retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated. Results Two hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS. Conclusion All chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation. Key Points • All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. • Chest CT scoring system and TSS had the highest specificity. • Both TSS and m-TSS consumed the least time compared to the other three scoring systems. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08432-5.

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“…In some studies, the algorithms had to differentiate between COVID-19 pneumonia and bacterial or viral pneumonias, whereas Harmon et al used CT studies of any clinical indication or staging CTs. The high variability in the specificity of the AI solutions in differentiating COVID-19 pneumonia from other conditions is due to the broad spectrum of other pulmonary conditions that mimic those of COVID-19, such as atypical bacterial pneumonias, Pneumocystis jirovecii pneumonia, pulmonary edema, and hypersensitivity pneumonia [ 22 ]. Findings on CT are nonspecific and can overlap even with non-infectious diseases [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

et al. 2021

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Objectives In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. Methods Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). Results Sensitivity and specificity ranges were 62–96% and 31–80%, respectively. Negative and positive predictive values ranged between 82–99% and 19–25%, respectively. AUC was in the range 0.54–0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54–0.69. Conclusions This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. Key Points • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.

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Mimickers of novel coronavirus disease 2019 (COVID-19) on chest CT: spectrum of CT and clinical features

Cited by 14 publications

References 54 publications

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients

Comparison of chest CT severity scoring systems for COVID-19

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

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