The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
COVID-19 pneumonia: what has CT taught us?
Open accessAbstract
In late December, 2019, a cluster of cases of viral pneumonia was linked to a seafood market in Wuhan (Hubei, China), and was later determined to be caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously known as 2019-nCoV). The genome sequence of SARS-CoV-2 is similar to, but distinct from, those of two other coronaviruses responsible for large-scale outbreaks in the past: severe acute respiratory syndrome coronavirus (SARS-CoV; about 79% sequence identity) and Middle East respiratory syndrome coronavirus (MERS-CoV; about 50%). CT has been an important imaging modality in assisting in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19) pneumonia, and reports on the radiological appearances of COVID-19 pneumonia are emerging. In The Lancet Infectious Diseases, Heshui Shi and colleagues discuss the CT findings and temporal changes of COVID-19 pneumonia with reference to the time of onset of symptoms, in the largest cohort thus far reported. The predominant CT findings included ground-glass opacification, consolidation, bilateral involve ment, and peripheral and diffuse distribution. These findings concur with other reports in smaller cohorts and with our own experience. Notably, in Shi and colleagues’ study, the asymptomatic (subclinical) group of patients showed early CT changes, supporting what was first observed in a familial cluster with COVID-19 pneumonia. Conversely, other studies have shown positive RT-PCR results for SARS-CoV-2 in the absence of CT changes, or abnormal CT findings with initial falsenegative RT-PCR results. As the epidemic evolves, we are starting to observe the varied presentations of COVID-19 pneumonia, with symptomatic patients showing concordant CT and RT-PCR findings. Nevertheless, this small number of individuals with COVID-19 pneumonia poses a diagnostic dilemma given the varied manifestations. The evolution of the disease on CT is not well understood. Shi and colleagues reported the presence of unilateral ground-glass opacities in a subgroup of 15 asymptomatic patients with COVID-19 pneumonia, substantiating previous anecdotal reports that asymptomatic patients could have CT changes before symptom onset. This finding suggests that CT is a sensitive modality with which to detect COVID-19 pneumonia, even in asymptomatic individuals, and could be considered as a screening tool—together with RT-PCR—when a patient has significant travel history or has had close contact with an infected individual. Furthermore, CT might be a particularly important screening tool in the small proportion of patients who have false-negative RT-PCR results. Shi and colleagues also showed that the lesions that were present in asymptomatic individuals progressed to bilateral diffuse disease with consolidation at around the first to second week after symptom onset. In Hubei, there was a surge of diagnoses of COVID-19 on Feb 12 because of the introduction of new diagnostic criteria that included CT changes. These criteria were employed to ensure timely treatment and isolation measures, because of the delays associated with laboratory testing and a large number of patients presenting with respiratory symptoms in the province. As the predominant pattern seen in COVID-19 pneumonia is ground-glass opacification, detecting COVID-19 with use of chest radiography—on which this type of abnormality is often imperceivable, particularly in patients with few symptoms or low severity—is likely to be challenging. By contrast, chest radiographs were used frequently in the diagnosis of SARS as both ground-glass opacification and consolidation were present early. The current literature is partly skewed by the geographical distribution of COVID-19 pneumonia and the preferential use of CT over chest radiograph in China. This preference might be due to the ease of access to CT in China and the lack of requirement for intravenous contrast agent for the examination. Therefore, it is unclear whether the threshold for performing CT evaluation of potential lung changes should be lower when chest radiographs are normal. Further research is needed to better select patients for CT examination, to define the utility of CT in COVID-19 pneumonia, and to explore the application of artificial intelligence in screening chest radiographs in suspected cases. Overall, we congratulate Shi and colleagues in adding valuable information to the current literature. The authors carefully evaluated the CT findings in a large cohort of patients with COVID-19 pneumonia, providing indirect evidence of the evolution of the CT changes with reference to the onset of symptoms. There is more to be learnt about this novel contagious viral pneumonia; more El se vi er
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751
Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020) · Radiology · doi:10.1148/radiol.2020200343 · Cited by 1,960
Cited by
Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19
Liao, M., Liu, Y., Yuan, J., Wen, Y., Xu, G., Zhao, J., Cheng, L., Li, J., Wang, X., Wang, F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0901-9 · Cited by 2,614
He, J.-L., Luo, L., Luo, Z.-D., Lyu, J.-X., Ng, M.-Y., Shen, X.-P., & Wen, Z. (2020) · Respiratory Medicine · doi:10.1016/j.rmed.2020.105980 · Cited by 281
Self-citation
Metadata
- Authors
- Lee, E. Y. P., Ng, M.-Y., & Khong, P.-L.
- Venue
- The Lancet Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 20
- Issue
- 4
- Pages
- 384-385
- ISSN
- 1473-3099 (ISSN-L)
- OpenAlex
- W3006792870
- Cited by
- 355
- References
- 2
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: COVID-19 pneumonia: what has CT taught us? https://app.cochat.ai/papers/10.1016/s1473-3099(20)30134-1
- The paper
- Lee, E. Y. P., Ng, M.-Y., & Khong, P.-L. (2020). COVID-19 pneumonia: what has CT taught us? The Lancet Infectious Diseases. https://doi.org/10.1016/s1473-3099(20)30134-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.