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19.05.2022 | Imaging Informatics and Artificial Intelligence

Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis

verfasst von: Yuanzhen Li, Yujie Liu, Yingying Liang, Ruili Wei, Wanli Zhang, Wang Yao, Shiwei Luo, Xinrui Pang, Ye Wang, Xinqing Jiang, Shengsheng Lai, Ruimeng Yang

Erschienen in: European Radiology | Ausgabe 11/2022

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Abstract

Objective

(1) To evaluate the diagnostic performance of radiomics in differentiating high-grade glioma from brain metastasis and how to improve the model. (2) To assess the methodological quality of radiomics studies and explore ways of embracing the clinical application of radiomics.

Methods

Studies using radiomics to differentiate high-grade glioma from brain metastasis published by 26 July 2021 were systematically reviewed. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. Pooled sensitivity and specificity of the radiomics model were also calculated.

Results

Seventeen studies combining 1,717 patients were included in the systematic review, of which 10 studies without data leakage suspicion were employed for the quantitative statistical analysis. The average RQS was 5.13 (14.25% of total), with substantial or almost perfect inter-rater agreements. The inclusion of clinical features in the radiomics model was only reported in one study, as was the case for publicly available algorithm code. The pooled sensitivity and specificity were 84% (95% CI, 80–88%) and 84% (95% CI, 81–87%), respectively. The performances of feature extraction from the volume of interest (VOI) or (semi) automatic segmentation in the radiomics models were superior to those of protocols employing region of interest (ROI) or manual segmentation.

Conclusion

Radiomics can accurately differentiate high-grade glioma from brain metastasis. The adoption of standardized workflow to avoid potential data leakage as well as the integration of clinical features and radiomics are advised to consider in future studies.

Key Points

• The pooled sensitivity and specificity of radiomics for differentiating high-grade gliomas from brain metastasis were 84% and 84%, respectively.

• Avoiding potential data leakage by adopting an intensive and standardized workflow is essential to improve the quality and generalizability of the radiomics model.

• The application of radiomics in combination with clinical features in differentiating high-grade gliomas from brain metastasis needs further validation.

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Titel: Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis
verfasst von: Yuanzhen Li
Yujie Liu
Yingying Liang
Ruili Wei
Wanli Zhang
Wang Yao
Shiwei Luo
Xinrui Pang
Ye Wang
Xinqing Jiang
Shengsheng Lai
Ruimeng Yang
Publikationsdatum: 19.05.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 11/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08828-x

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Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis