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. 2019 Aug 22:9:806.
doi: 10.3389/fonc.2019.00806. eCollection 2019.

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Chaoyue Chen  1   2   3 , Xuejin Ou  1   2   4 , Jian Wang  5 , Wen Guo  1   2   3   4 , Xuelei Ma  1   2
Affiliations

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Chaoyue Chen et al. Front Oncol. .

Abstract

Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs). Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linear Discriminant Analysis (LDA) as the classification algorithm. In the training group, the sensitivity, specificity, accuracy, and AUC were 0.75, 0.85, 0.80, and 0.80, respectively; and in the testing group, the sensitivity, specificity, accuracy, and AUC of the model were 0.69, 0.86, 0.78, and 0.80, respectively. The second model was the Distance Correlation as the selection method and logistic regression (LR) as the classification algorithm, with sensitivity, specificity, accuracy, and AUC of 0.75, 0.85, 0.80, 0.80 in the training group and 0.69, 0.86, 0.78, 0.80 in the testing group. Conclusion: Radiomic-based machine learning has potential to be utilized in differentiating GBM from MBTs.

Keywords: glioblastomas; machine learning; metastatic brain tumors; radiomics; texture analysis.

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Figures

Figure 1
Figure 1
The magnetic resonance images (T1C) of a patient with (A) GBM and (B) MBTs.
Figure 2
Figure 2
Screen capture of regions of interest (ROI) delineation.
Figure 3
Figure 3
Heat map of the classifiers for differentiating between GBM and MBTs. (A) The AUC of the training group. (B) The AUC of the testing group.
Figure 4
Figure 4
Distribution of the discriminant functions of LDA in discriminating GBM from MBTs.
Figure 5
Figure 5
Example of distributions of the LDA function of (A) MBTs and (B) GBM for one cycle.
See this image and copyright information in PMC

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