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A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas.

Authors :
Wang, Jie
Hu, Yue
Zhou, Xuejun
Bao, Shanlei
Chen, Yue
Ge, Min
Jia, Zhongzheng
Source :
European Journal of Radiology. Feb2022, Vol. 147, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• Radiomics is a rapidly growing discipline, which combines the advantages of high throughput and noninvasiveness. • Isocitrate dehydrogenase (IDH) has been shown to have both diagnostic and prognostic implications in gliomas. Angiogenesis is a major histological feature and closely related to the degree of aggressiveness in gliomas. • The radiomics model based on DCE-MRI and DWI has an efficient performance in estimating IDH1 mutation and angiogenesis in gliomas. To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas. One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models. For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919). The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0720048X
Volume :
147
Database :
Academic Search Index
Journal :
European Journal of Radiology
Publication Type :
Academic Journal
Accession number :
154893456
Full Text :
https://doi.org/10.1016/j.ejrad.2021.110141