Abstract
Objectives
To determine whether diffusion- and perfusion-weighted MRI–based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs)
Methods
Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning–based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).
Results
The multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.
Conclusion
Multiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.
Key Points
• The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation.
• The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading.
• Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.
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Abbreviations
- ADC:
-
Apparent diffusion coefficient
- CBV:
-
Cerebral blood volume
- CE:
-
Contrast-enhanced
- CNS:
-
Central nervous system
- DSC:
-
Dynamic susceptibility contrast
- DWI:
-
Diffusion-weighted imaging
- FLAIR:
-
Fluid-attenuated inversion recovery
- IDH:
-
Isocitrate dehydrogenase
- LGG:
-
Lower grade glioma
- T1WI:
-
T1-weighted imaging
- WHO:
-
2016 World Health Organization
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Funding
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (grant number NRF-2017R1A2A2A05001217 and grant number NRF-2017R1C1B2007258)
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The scientific guarantor of this publication is Jeong Hoon Kim.
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Kim, M., Jung, S.Y., Park, J.E. et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol 30, 2142–2151 (2020). https://doi.org/10.1007/s00330-019-06548-3
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DOI: https://doi.org/10.1007/s00330-019-06548-3