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MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis

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Abstract

Objective

To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis.

Methods

270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set.

Results

A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage).

Conclusions

A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible.

Key Points

• EGFR expression status is an important biomarker for gliomas.

• EGFR in lower grade gliomas could be predicted using radiogenomic analysis.

• A logistic regression model is an efficient approach for analysing radiomic features.

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Abbreviations

AUC:

Area under the curve

EGFR:

Epidermal growth factor receptor

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

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Author information

Authors and Affiliations

  1. Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China

    Yiming Li, Xing Liu, Zenghui Qian, Xing Fan, Shaowu Li & Tao Jiang

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Kaibin Xu

  3. Department of Neuroradiology, Beijing Tiantan Hospital, Beijing, China

    Kai Wang & Yinyan Wang

  4. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China

    Yinyan Wang

Authors
  1. Yiming Li
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  2. Xing Liu
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  3. Kaibin Xu
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  4. Zenghui Qian
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  5. Kai Wang
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  6. Xing Fan
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  7. Shaowu Li
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  8. Yinyan Wang
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  9. Tao Jiang
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Corresponding authors

Correspondence to Yinyan Wang or Tao Jiang.

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Guarantor

The scientific guarantor of this publication is Tao Jiang.

Conflict of interest

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.

Funding

This study has received funding by the National Natural Science Foundation of China (No. 81601452).

Statistics and biometry

Mr Kaibin Xu kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was not required for this study because the data were collected retrospectively in the study.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Yiming Li and Xing Liu contributed equally to this work

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Li, Y., Liu, X., Xu, K. et al. MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol 28, 356–362 (2018). https://doi.org/10.1007/s00330-017-4964-z

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  • DOI: https://doi.org/10.1007/s00330-017-4964-z

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