Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis

Moran Artzi, Gilad Liberman, Deborah T. Blumenthal, Orna Aizenstein, Felix Bokstein, Dafna Ben Bashat*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: High-grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging. Purpose: To use radiomics patch-based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components. Study Type: Prospective. Subjects: In all, 179 MRI scans were obtained from 102 patients: 67 patients with HGG and 35 patients with brain metastases. A subgroup of 15 patients with HGG were scanned before and following administration of bevacizumab. Field Strength/Sequence: Pre and postcontrast agent T1-weighted-imaging (WI), T2WI, FLAIR, diffusion-tensor-imaging (DTI), and dynamic-contrast-enhanced (DCE)-MRI at 3T. Assessment: A total of 225 histograms and gray-level-co-occurrence matrix-based features were extracted from the nonenhancing lesion area. Tumoral volumes of interest (VOIs) were defined at the peritumoral area in patients with HGG; nontumoral VOIs were defined in patients with brain metastasis. Twenty machine-learning algorithms including support-vector-machine (SVM), k-nearest neighbor, decision-trees, and ensemble classifiers were tested. The best classifier was trained on the entire labeled data, and was used to classify the entire data. Statistical Tests: Dimensional reduction was performed on the 225 features using principal component analysis. Classification results were evaluated based on the sensitivity, specificity, and accuracy of each of the 20 classifiers, first based on a training and testing dataset (80% of the labeled data) in a 5-fold manner, and next by applying the best classifier to the validation data (the remaining 20% of the labeled data). Results were additionally evaluated by assessing differences in dynamic-contrast-enhanced plasma-volume (vp) and volume-transfer-constant (ktrans) values between the two components using Mann–Whitney U-test/t-test. Results: The best classification into tumoral and nontumoral lesion components was obtained using a linear SVM classifier, with average accuracy of 87%, sensitivity 86%, and specificity of 89% (for the training and testing data). Significantly higher vp and ktrans values (P < 0.0001) were detected in the tumoral compared to the nontumoral component. Preliminary classification results in a subgroup of patients treated with bevacizumab demonstrated a reduction mainly in the nontumoral component following administration of bevacizumab, enabling early assessment of disease progression in some patients. Data Conclusion: A radiomics patch-based analysis enables classification of the nonenhancing lesion area in patients with HGG. Preliminary results were promising and the proposed method has the potential to assist in clinical decision-making and to improve therapy response assessment in patients with HGG. Level of Evidence: 1. Technical Efficacy Stage 4. J. Magn. Reson. Imaging 2018;48:729–736.

Original languageEnglish
Pages (from-to)729-736
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Issue number3
StatePublished - Sep 2018


  • MRI
  • high grade gliomas
  • machine-learning
  • texture-patch-based analysis
  • tumor classification


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