A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Abstract

Introduction: Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. Materials and methods: This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. Results: A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis

“…To date, no systematic review or meta-analysis has been performed that includes all types of imaging techniques to diagnose COVID-19 and other pneumonias. Kao et al [51] evaluated the CT-based radiomics signature model to successfully distinguish COVID-19 from other viral pneumonias, and came to similar conclusions as ours, with high study heterogeneity. They assessed studies up to February 26, 2021, so only 6 studies were included, and all studies were conducted in China.…”

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

“…Most of the radiomics studies were oncological, but radiomics has potential clinical application in the non-oncological field [ 30 ]. Several reviews have summarized the role of radiomics in non-oncological diseases, including mild cognitive impairment and Alzheimer’s disease [ 15 ], COVID-19 and viral pneumonia [ 16 ], and cardiac diseases [ 17 ]. The study quality evaluated by RQS was the main concern of these reviews.…”

“…We suspected that the COVID-19 and viral pneumonia review reached a better RQS rating since the included studies were published recently with a relatively larger sample size, which allow adequate feature reduction and external validation. Actually, none of the studies in this review lacked the feature reduction, and all the studies performed validation [ 16 ]. In contrast, a significant number of previous studies did not perform feature reduction and validation.…”

A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity

“…The RQS values of the included literature ranged from seven to 16; thus, the highest RQS in the selected studies was only 40%. A previous meta-analysis also found a maximum RQS score of 16 for CT-based texture features used to differentiate between COVID-19 and viral pneumonia [ 14 ]. Compared with this study, a low RQS score makes it challenging to conduct a high-quality radiomics study in current research settings.…”

“…In previous studies on COVID-19, machine learning CT-based radiomics has been shown to help diagnose and differentiate COVID-19 pneumonia from pneumonia caused by other pathogens [ 12 – 14 ]. Additionally, CT-based radiomics reportedly predicts the severity and outcome of COVID-19 pulmonary opacities [ 15 ].…”