The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis

Abstract

Background

Quantitative evaluation of the effect of glioblastoma (GBM) heterogeneity on survival stratification would be critical for the diagnosis, treatment decision, and follow-up management.

Purpose

To evaluate the effect of GBM heterogeneity on survival stratification, using texture analysis on multimodal magnetic resonance (MR) imaging.

Material and Methods

A total of 119 GBM patients (65 in long-term and 54 in short-term survival group, separated by overall survival of 12 months) were selected from the Cancer Genome Atlas, who underwent the T1-weighted (T1W) contrast-enhanced (CE), T1W, T2-weighted (T2W), and FLAIR sequences. For each sequence, the co-occurrence matrix, run-length matrix, and histogram features were extracted to reflect GBM heterogeneity on different scale. The recursive feature elimination based support vector machine was adopted to find an optimal subset. Then the stratification performance of four MR sequences was evaluated, both alone and in combination.

Results

When each sequence used alone, the T1W-CE sequence performed best, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.7915, 80.67%, 78.45%, and 83.33%, respectively. When the four sequences combined, the stratification performance was basically equal to that of T1W-CE sequence. In the optimal subset of features extracted from multimodality, those from the T2W sequence weighted the most.

Conclusion

All the four sequences could reflect heterogeneous distribution of GBM and thereby affect the survival stratification, especially T1W-CE and T2W sequences. However, the stratification performance using only the T1W-CE sequence can be preserved with omission of other three sequences, when investigating the effect of GBM heterogeneity on survival stratification.

Keywords

Glioblastoma heterogeneity magnetic resonance (MR) imaging recursive feature elimination based support vector machine (SVM-RFE)prognosis

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