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. 2023 Jun 19;14(1):111.
doi: 10.1186/s13244-023-01437-2.

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Jingyu Zhong #  1 , Junjie Lu #  2 , Guangcheng Zhang  3 , Shiqi Mao  4 , Haoda Chen  5 , Qian Yin  6 , Yangfan Hu  1 , Yue Xing  1 , Defang Ding  1 , Xiang Ge  1 , Huan Zhang  7 , Weiwu Yao  8
Affiliations

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Jingyu Zhong et al. Insights Imaging. .

Abstract

Objective: To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level.

Methods: 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.

Results: 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.

Conclusions: The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence.

Clinical relevance statement: 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.

Keywords: Meta-analysis; Quality improvement; Radiomics; Systematic review.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of systematic review search and selection
Fig. 2
Fig. 2
Result of quality assessment of included systematic reviews. a PRISMA adherence rate, b AMASTR-2 rating score, and c ROBIS assessment
Fig. 3
Fig. 3
Summary of evidence rating. AML = angiomyolipoma, BC = breast cancer, HCC = hepatocellular carcinoma, NSCLC = non-small cell lung cancer, RCC = renal clear cell carcinoma, NACT = neoadjuvant chemotherapy, TNBC = triple negative breast cancer. NS = not significant, Sig = significant, N/a = not applicable
See this image and copyright information in PMC

References

    1. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036. - DOI - PMC - PubMed
    1. Tomaszewski MR, Gillies RJ. The biological meaning of radiomic features. Radiology. 2021;298(3):505–516. doi: 10.1148/radiol.2021202553. - DOI - PMC - PubMed
    1. Gillies RJ, Kinahan PE. Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–577. doi: 10.1148/radiol.2015151169. - DOI - PMC - PubMed
    1. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–762. doi: 10.1038/nrclinonc.2017.141. - DOI - PubMed
    1. O’Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–186. doi: 10.1038/nrclinonc.2016.162. - DOI - PMC - PubMed

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