Zhong Jingyu, Lu Junjie, Zhang Guangcheng, Mao Shiqi, Chen Haoda, Yin Qian, Hu Yangfan, Xing Yue, Ding Defang, Ge Xiang, Zhang Huan, Yao Weiwu
Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Insights Imaging. 2023 Jun 19;14(1):111. doi: 10.1186/s13244-023-01437-2.
To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level.
A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated.
We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation.
The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence.
The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
对评估放射组学研究质量和证据水平的荟萃分析进行概述。
通过同行评审的电子数据库、预印本服务器和系统评价方案登记册进行系统检索，直至2022年11月15日。纳入对原发性放射组学研究进行荟萃分析的系统评价。分别采用PRISMA（系统评价和荟萃分析的首选报告项目）2020清单、AMSTAR-2（评估系统评价的测量工具，第2版）工具和ROBIS（系统评价中的偏倚风险）工具评估其报告透明度、方法学质量和偏倚风险。对支持放射组学临床应用的证据水平进行评级。
我们确定了44项关于放射组学研究的荟萃分析系统评价。PRISMA依从率的平均值±标准差为65±9%。AMSTAR-2工具将5项和39项系统评价分别评为低置信度和极低置信度。ROBIS评估显示，分别有5项、11项和28项系统评价存在低、不明确和高风险。我们在38项纳入的系统评价中重新进行了53项荟萃分析。分别有3项、7项和43项荟萃分析被评为具有令人信服、高度提示和弱证据水平。令人信服的证据水平出现在以下情况：（1）T2-FLAIR放射组学用于低级别胶质瘤中异柠檬酸脱氢酶（IDH）突变型与IDH野生型的鉴别，（2）CT放射组学用于新型冠状病毒肺炎（COVID-19）与其他病毒性肺炎的鉴别，以及（3）MRI放射组学用于高级别胶质瘤与脑转移瘤的鉴别。
关于放射组学的系统评价质量欠佳。仅有有限数量的放射组学方法得到了令人信服的证据水平支持。
支持放射组学临床应用的证据不足，需要开展研究将放射组学从学术工具转化为切实可行的临床辅助手段以实现临床应用。
