Abstract：
BACKGROUND: 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.
METHODS: 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.
CONCLUSIONS: This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.
METHODS: 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.
CONCLUSIONS: This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.
摘要：
背景：根据中国卫生委员会指南，2019年冠状病毒病（COVID-19）的严重程度被归类为轻度，中度,严重,或批判。COVID-19在重症和危重症患者中的死亡率较高；因此，早期发现COVID-19可预防疾病进展并提高患者生存率.计算机断层扫描（CT）影像组学，作为一种机器学习方法，提供了对COVID-19肺炎的客观和数学评估。由于基于CT的影像组学研究最近集中在COVID-19诊断和严重程度分析上，本荟萃分析旨在研究基于CT的影像组学模型在确定COVID-19严重程度方面的预测能力.
方法：本研究遵循PRISMA指南的诊断版本。PubMed,Embase数据库和Cochrane中央对照试验登记册，并搜索了Cochrane系统评价数据库，以识别从开始到2021年7月16日的荟萃分析中的相关文章。使用森林地块分析敏感性和特异性。使用汇总接收器工作特性曲线计算总预测能力。使用漏斗图评估偏倚。使用影像组学质量评分和诊断准确性研究工具的质量评估评估纳入的文献的质量。
结果：影像组学质量评分范围为7至16（可达到的评分：22128至36）。合并的敏感性和特异性分别为0.800（95％置信区间[CI]0.662-0.891）和0.874（95％CI0.773-0.934），分别。接收器工作特征曲线下的汇集面积为0.908。质量评估工具显示出良好的结果。
结论：这项荟萃分析表明，基于CT的影像组学模型可能有助于预测COVID-19肺炎的严重程度。
方法：本研究遵循PRISMA指南的诊断版本。PubMed,Embase数据库和Cochrane中央对照试验登记册，并搜索了Cochrane系统评价数据库，以识别从开始到2021年7月16日的荟萃分析中的相关文章。使用森林地块分析敏感性和特异性。使用汇总接收器工作特性曲线计算总预测能力。使用漏斗图评估偏倚。使用影像组学质量评分和诊断准确性研究工具的质量评估评估纳入的文献的质量。
结果：影像组学质量评分范围为7至16（可达到的评分：22128至36）。合并的敏感性和特异性分别为0.800（95％置信区间[CI]0.662-0.891）和0.874（95％CI0.773-0.934），分别。接收器工作特征曲线下的汇集面积为0.908。质量评估工具显示出良好的结果。
结论：这项荟萃分析表明，基于CT的影像组学模型可能有助于预测COVID-19肺炎的严重程度。
