BACKGROUND AND PURPOSE: The heterogeneity of glioblastoma contributes to the poor and variant prognosis. The aim of this retrospective study was to assess the glioblastoma heterogeneity with MR imaging textures and to evaluate its impact on survival time. MATERIALS AND METHODS: A total of 133 patients with primary glioblastoma who underwent postcontrast T1-weighted imaging (acquired before treatment) and whose data were filed with the survival times were selected from the Cancer Genome Atlas. On the basis of overall survival, the patients were divided into 2 groups: long-term (≥12 months, n = 67) and short-term (<12 months, n = 66) survival. To measure heterogeneity, we extracted 3 types of textures, co-occurrence matrix, run-length matrix, and histogram, reflecting local, regional, and global spatial variations, respectively. Then the support vector machine classification was used to determine how different texture types perform in differentiating the 2 groups, both alone and in combination. Finally, a recursive feature-elimination method was used to find an optimal feature subset with the best differentiation performance. RESULTS: When used alone, the co-occurrence matrix performed best, while all the features combined obtained the best survival stratification. According to feature selection and ranking, 43 top-ranked features were selected as the optimal subset. Among them, the top 10 features included 7 run-length matrix and 3 co-occurrence matrix features, in which all 6 regional run-length matrix features emphasizing high gray-levels ranked in the top 7. CONCLUSIONS: The results suggest that local and regional heterogeneity may play an important role in the survival stratification of patients with glioblastoma.

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.