A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity.
Radiol Med
; 127(7): 754-762, 2022 Jul.
Article
in English
| MEDLINE | ID: covidwho-1899296
ABSTRACT
INTRODUCTION:
According to the Chinese Health Commission guidelines, coronavirus disease 2019 (COVID-19) severity is classified as mild, moderate, severe, or critical. The mortality rate of COVID-19 is higher among patients with severe and critical diseases; therefore, early identification of COVID-19 prevents disease progression and improves patient survival. Computed tomography (CT) radiomics, as a machine learning method, provides an objective and mathematical evaluation of COVID-19 pneumonia. As CT-based radiomics research has recently focused on COVID-19 diagnosis and severity analysis, this meta-analysis aimed to investigate the predictive power of a CT-based radiomics model in determining COVID-19 severity. MATERIALS ANDMETHODS:
This study followed the diagnostic version of PRISMA guidelines. PubMed, Embase databases and the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews were searched to identify relevant articles in the meta-analysis from inception until July 16, 2021. The sensitivity and specificity were analyzed using forest plots. The overall predictive power was calculated using the summary receiver operating characteristic curve. The bias was evaluated using a funnel plot. The quality of the included literature was assessed using the radiomics quality score and quality assessment of diagnostic accuracy studies tool.RESULTS:
The radiomics quality scores ranged from 7 to 16 (achievable score 2212 8 to 36). The pooled sensitivity and specificity were 0.800 (95% confidence interval [CI] 0.662-0.891) and 0.874 (95% CI 0.773-0.934), respectively. The pooled area under the receiver operating characteristic curve was 0.908. The quality assessment tool showed favorable results.CONCLUSION:
This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
COVID-19 Testing
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
Radiol Med
Year:
2022
Document Type:
Article
Affiliation country:
S11547-022-01510-8
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