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Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma

European Radiology volume 30, pages 2142–2151 (2020)Cite this article

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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Author notes

  1. Minjae Kim and So Yeong Jung equally contributed to this work.

Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea

    Minjae Kim, So Yeong Jung, Ji Eun Park & Ho Sung Kim

  2. Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea

    Yeongheun Jo & Jeong Hoon Kim

  3. Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea

    Seo Young Park

  4. Deparment of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, South Korea

    Soo Jung Nam

Authors
  1. Minjae Kim
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Corresponding author

Correspondence to Ji Eun Park.

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The scientific guarantor of this publication is Jeong Hoon Kim.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise (Seo Young Park, 8 years of experience).

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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

Keywords

  • Glioma
  • Magnetic resonance imaging
  • Isocitrate dehydrogenase
  • Neoplasm grading
  • Machine learning