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Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model.

Methods

Sixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into training and validation data sets (about 2:1). Quantitative radiomic features of the tumours were extracted from contrast-enhanced MR images. A subset of features was selected by feature stability and Boruta algorithm. The selected features were used to build a decision tree model. Predictive accuracy, sensitivity and specificity were used to assess model performance. The classification outcome of the model was combined with tumour location, age and gender features, and multivariable logistic regression analysis and permutation test using the entire data set were performed to further evaluate the decision tree model.

Results

A total of 271 radiomic features were successfully extracted for each tumour. Twelve features were selected as input variables to build the decision tree model. Two features S(1, -1) Entropy and S(2, -2) SumAverg were finally included in the model. The model showed an accuracy, sensitivity and specificity of 0.87, 0.90 and 0.83 for the training data set and 0.86, 0.80 and 0.91 for the validation data set. The classification outcome of the model related to the actual tumour types and did not rely on the other three features (p < 0.001).

Conclusions

A decision tree model with two features derived from the contrast-enhanced MR images performed well in differentiating PA from GBM.

Key Points

• MRI findings of PA and GBM are sometimes very similar.

• Radiomics provides much more quantitative information about tumours.

• Radiomic features can help to distinguish PA from GBM.

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Abbreviations

CI:

Confidence interval

CNS:

Central nervous system

CP:

Complexity parameter

GBM:

Glioblastoma

ICC:

Intraclass correlation coefficient

MRI:

Magnetic resonance imaging

PA:

Pilocytic astrocytoma

ROI:

Regions of interest

SD:

Standard deviation

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Funding

This study has received funding by the medicine and health scientific and technological program of Zhejiang province, China (2016KYA104 and 2017KY374)

Author information

Authors and Affiliations

  1. Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China

    Fei Dong, Qian Li, Duo Xu, Xiuliang Zhu, Fangfang Xu, Biao Jiang & Minming Zhang

  2. Department of Radiology, Fujian Provincial Hospital, Fuzhou, 350001, China

    Wenji Xiu

  3. Department of Neurosurgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China

    Qiang Zeng

Authors
  1. Fei Dong
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  2. Qian Li
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  3. Duo Xu
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  4. Wenji Xiu
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  6. Xiuliang Zhu
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  7. Fangfang Xu
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  8. Biao Jiang
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  9. Minming Zhang
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Corresponding authors

Correspondence to Qian Li or Minming Zhang.

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Guarantor

The scientific guarantor of this publication is Minming Zhang.

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 has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because this is a retrospective study on the images.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicentre study

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Dong, F., Li, Q., Xu, D. et al. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol 29, 3968–3975 (2019). https://doi.org/10.1007/s00330-018-5706-6

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  • DOI: https://doi.org/10.1007/s00330-018-5706-6

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