A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Khatami, Fatemeh; Saatchi, Mohammad; Zadeh, Seyed Saeed Tamehri; Aghamir, Zahra Sadat; Shabestari, Alireza Namazi; Reis, Leonardo Oliveira; Aghamir, Seyed Mohammad Kazem

doi:10.1038/s41598-020-80061-2

Cited by 102 publications

(85 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Concerning the diagnostic performance of CT scan, a recent meta-analysis reported sensitivity and specificity of 87% and 46%, respectively. Despite a good diagnostic sensitivity, this meta-analysis recommended to use RT-PCR besides CT scan to reach the most accurate result [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Can initial chest CT scan predict status and clinical outcomes of COVID-19 infection? A retrospective cohort study

Abdollahi

Nabahati

Javanian

et al. 2021

Egypt J Radiol Nucl Med

View full text Add to dashboard Cite

Background We aimed to investigate the association of initial chest CT scan findings with status and adverse outcomes of COVID-19 (including ICU admission, mortality, and disease severity). This retrospective cohort study was performed in three hospitals in Babol, northern Iran, between February and March 2020. Cases were confirmed by real-time polymerase chain reaction (RT-PCR). Clinical and paraclinical data of the patients were collected from their medical records. CT severity score (CSS) was calculated by a senior radiologist. Disease severity was determined based on the World Health Organization criteria. Results In total, 742 patients were included, of whom 451 (60.8%) were males and 291 (39.2%) were females. The mean age was 56.59 ± 14.88 years old. Also, 523 (70.5%) were RT-PCR-positive. Ground glass opacity was directly associated with RT-PCR positivity (odds ratio [OR] = 2.07). Also, RT-PCR-positive cases had significantly a higher CSS than RT-PCR-negative cases (p = 0.037). In patients confirmed with COVID-19, peribronchovascular distribution of lesions, number of zones involved, and CSS were associated with increased risk of ICU admission (OR = 2.93, OR = 2.10, and OR = 1.14, respectively), mortality (OR = 2.30, OR = 1.35, and OR=1.08, respectively), severe disease (OR = 2.06, OR = 1.68, and OR = 1.10, respectively), and critical disease (OR = 4.62, OR = 3.21, and OR = 1.23, respectively). Also, patients who had consolidation were at a higher risk of severe disease compared with those who did not (OR = 4.94). Conclusion Initial chest CT scan can predict COVID-19 positivity, ICU admission, mortality, and disease severity, specifically through CSS.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Chest computed tomography (CT) scan is an important method for the diagnosis of COVID-19 pneumonia [ 5 ]. Being more time-saving and having a comparable sensitivity versus RT-PCR assay make CT scan a powerful tool for rapid screening of the suspected cases [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Can initial chest CT scan predict status and clinical outcomes of COVID-19 infection? A retrospective cohort study

Abdollahi

Nabahati

Javanian

et al. 2021

Egypt J Radiol Nucl Med

View full text Add to dashboard Cite

show abstract

“…On the other hand, good values for specificity (range: 75.6%e87.4%) and NPV (range: 74.2%e 78.6%) were obtained, in line with evidence reported in two recently published meta-analyses determining the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. 18,19 Nonetheless, nearly 25% of patients were incorrectly triaged as COVID-19 (þ). The latter could represent a major concern, for which reason additional clinical and laboratory tests should be considered in order to exclude other aetiologies, monitoring these patients through a dedicated care pathway, while preventing them from coming into close contact with confirmed COVID-19 cases as far as possible before a final diagnosis is made.…”

Section: Discussionmentioning

confidence: 99%

Radiographers and COVID-19 pneumonia: Diagnostic performance using CO-RADS

et al. 2021

View full text Add to dashboard Cite

Introduction: A more structured role of radiographers is advisable to speed up the management of patients with suspected COVID-19. The purpose of our study was to evaluate the diagnostic performance of radiographers in the detection of COVID-19 pneumonia on chest CT using CO-RADS descriptors. Methods: CT images of patients who underwent RT-PCR and chest CT due to COVID-19 suspicion between March and July 2020 were analysed retrospectively. Six readers, including two radiologists, two highly experienced radiographers and two less experienced radiographers, independently scored each CT using the CO-RADS lexicon. ROC curves were used to investigate diagnostic accuracy, and Fleiss'k statistics to evaluate inter-rater agreement. Results: 714 patients (419 men; 295 women; mean age: 64 years ±19SD) were evaluated. CO-RADS> 3 was identified as optimal diagnostic threshold. Highly experienced radiographers achieved an average sensitivity of 58.7% (95%CI: 52.5e64.7), an average specificity of 81.8% (95%CI: 77.9e85.2), and a mean AUC of 0.72 (95%CI: 0.68e0.75). Among less experienced radiographers, an average sensitivity of 56.3% (95%CI: 50.1e62.2) and an average specificity of 81.5% (95%CI: 77.6e84.9) were observed, with a mean AUC of 0.71 (95%CI: 0.68e0.74). Consultant radiologists achieved an average sensitivity of 60.0% (95%CI: 53.7e65.8), an average specificity of 81.7% (95%CI: 77.8e85.1), and a mean AUC of 0.73 (95%CI: 0.70 e0.77). Conclusion: Radiographers can adequately recognise the classic appearances of COVID-19 on CT, as described by the CO-RADS assessment scheme, in a way comparable to expert radiologists. Implications for practice: Radiographers, as the first healthcare professionals to evaluate CT images in patients with suspected SARS-CoV-2 infection, could diagnose COVID-19 pneumonia by means of a categorical reporting scheme at CT in a reliable way, hence playing a primary role in the early management of these patients.

show abstract

“…Reverse transcription polymerase chain reaction (RT-PCR) test [ 4 ] and chest computed tomography (CT) are the two important tools for confirming COVID-19 patients. The RT-PCR can be used for diagnosis, but occasionally repeated RT-RCR need to be tested to gain more accurate result [ 5 , 6 ]. Besides, RT-PCR test is time-consuming, which takes 4–6 h to obtain the results.…”

Section: Introductionmentioning

confidence: 99%

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

Wan

Zhou

Zhang

2021

Entropy

View full text Add to dashboard Cite

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.

show abstract

A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Cited by 102 publications

References 82 publications

Can initial chest CT scan predict status and clinical outcomes of COVID-19 infection? A retrospective cohort study

Can initial chest CT scan predict status and clinical outcomes of COVID-19 infection? A retrospective cohort study

Radiographers and COVID-19 pneumonia: Diagnostic performance using CO-RADS

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

Contact Info

Product

Resources

About