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Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study

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Abstract

Objectives

To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM).

Methods

In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.

Results

The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.

Conclusions

Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features.

Key Points

• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts.

• All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features.

• Combing clinical factors with radiomics features did not benefit the prediction performance.

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Abbreviations

AUC:

Area under the ROC curve

FLAIR:

Fluid-attenuated inversion recovery

GBM:

Glioblastoma multiforme

GLCM:

Grey-level co-occurrence matrix

GLRLM:

Grey-level run length matrix

GLSZM:

Grey level size zone matrix

KPS:

Karnofsky performance score

MGMT:

O6-methylguanine-DNA methyltransferase

MRI:

Magnetic resonance imaging

NGTDM:

Neighbourhood grey-tone difference matrix

ROC:

Receiver operating characteristic curve

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

VASARI:

Visually Accusable Rembrandt Images

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Funding

This study received funding by National Natural Science Foundation of China (No. 61571432), National Basic Research Program of China (973 Program, No. 2015CB755500), and Shenzhen Basic Research Program (JCYJ20170413162354654).

Author information

Author notes

  1. Zhi-Cheng Li and Hongmin Bai contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhi-Cheng Li, Qiuchang Sun, Qihua Li, Lei Liu & Hairong Zheng

  2. Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China

    Hongmin Bai

  3. Department of Radiology, The 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Yan Zou

  4. Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

    Yinsheng Chen

  5. Department of Neurosurgery, The 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Chaofeng Liang

Authors
  1. Zhi-Cheng Li
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  2. Hongmin Bai
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  3. Qiuchang Sun
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  4. Qihua Li
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  9. Hairong Zheng
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Corresponding authors

Correspondence to Yinsheng Chen or Chaofeng Liang.

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Guarantor

The scientific guarantor of this publication is Hairong Zheng.

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.

Statistics and biometry

One of the authors (Zhi-Cheng Li) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the three local institutions. Institutional Review Board approval for the TCIA data was not required.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicentre study

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Li, ZC., Bai, H., Sun, Q. et al. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study. Eur Radiol 28, 3640–3650 (2018). https://doi.org/10.1007/s00330-017-5302-1

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  • DOI: https://doi.org/10.1007/s00330-017-5302-1

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