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Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature.

Methods

In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis.

Results

Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature.

Conclusion

These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.

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Abbreviations

PTEN:

Phosphatase and tensin homolog

mRMR:

Minimum redundancy maximum relevance

CE:

Contrast enhancement

T2:

T2-weighted

MR:

Magnetic resonance

TCGA:

The Cancer Genome Atlas

AUC:

Area under the curve

SVM:

Support vector machine

ROC:

Receiver operating characteristic

DAVID:

Database for Annotation, Visualization and Integrated Discovery

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Funding

This study was funded by the National Natural Science Foundation of China (No. 81601452), the Beijing Natural Science Foundation (No. 7174295), the National Key Research and Development Plan (No. 2016YFC0902500) and the National Key Research and Development Program of China (2018YFC0115604).

Author information

Author notes

  1. YLi and YLiang contributed equally to this work.

Authors and Affiliations

  1. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Yiming Li, Zhiyan Sun, Xing Fan & Xing Liu

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

    Yuchao Liang, Zhong Zhang & Yinyan Wang

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

    Kaibin Xu

  4. Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Shaowu Li

  5. Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing, China

    Tao Jiang

  6. China National Clinical Research Center for Neurological Diseases, Beijing, China

    Tao Jiang

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  1. Yiming Li
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  2. Yuchao Liang
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  3. Zhiyan Sun
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  4. Kaibin Xu
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  5. Xing Fan
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  6. Shaowu Li
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  8. Tao Jiang
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Correspondence to Yinyan Wang.

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Li, Y., Liang, Y., Sun, Z. et al. Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging. Neuroradiology 61, 1229–1237 (2019). https://doi.org/10.1007/s00234-019-02244-7

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