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European Radiology
Published in: European Radiology 8/2019

01-08-2019 | Oncology

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

Authors: Fei Dong, Qian Li, Duo Xu, Wenji Xiu, Qiang Zeng, Xiuliang Zhu, Fangfang Xu, Biao Jiang, Minming Zhang

Published in: European Radiology | Issue 8/2019

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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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Metadata
Title
Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features
Authors
Fei Dong
Qian Li
Duo Xu
Wenji Xiu
Qiang Zeng
Xiuliang Zhu
Fangfang Xu
Biao Jiang
Minming Zhang
Publication date
01-08-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5706-6

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