Evidence map›Paper›PMID 33135665›Full record

ReviewDiagnostic and interventional radiology (Ankara, Turkey)2021

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review.

Jinkui Li, Ruifeng Yan, Yanan Zhai, Xiaolong Qi, Junqiang Lei

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostic and interventional radiology (Ankara, Turkey), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2025
    Article
  7. Article
  8. Review
  9. Surgical management for hip fracture in a COVID-19 patient: A case report and literature review.Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association · 2023
    Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Jinkui LiDepartment of Radiology, the First Hospital of Lanzhou University, Intelligent Imaging Medical Engineering Research Center, Accurate Image Collaborative Innovation International Science and Technology Cooperation, Lanzhou, China.
Ruifeng YanDepartment of Radiology, the First Hospital of Lanzhou University, Intelligent Imaging Medical Engineering Research Center, Accurate Image Collaborative Innovation International Science and Technology Cooperation, Lanzhou, China.
Yanan ZhaiDepartment of Radiology, the First Hospital of Lanzhou University, Intelligent Imaging Medical Engineering Research Center, Accurate Image Collaborative Innovation International Science and Technology Cooperation, Lanzhou, China.
Xiaolong QiThe first Hospital of Lanzhou University, Lanzhou, Gansu, China.
Junqiang LeiDepartment of Radiology, the First Hospital of Lanzhou University, Intelligent Imaging Medical Engineering Research Center, Accurate Image Collaborative Innovation International Science and Technology Cooperation, Lanzhou, China.
First Hospital of Lanzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Indexed as

COVID-19HumansLungRetrospective StudiesSARS-CoV-2ThoraxTomography, X-Ray Computed

Identifiers

PMID33135665
PMCPMC8480948
OpenAlexW3094288636

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.