Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis

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A group 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, the authors 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. The results suggest that local and regional heterogeneity may play an important role in the survival stratification of patients with glioblastoma.

Abstract

Figure 5 from paper
Kaplan-Meier survival curves determined by the log-rank test (P < .001).

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.

 

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Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis
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jross
Jeffrey Ross • Mayo Clinic, Phoenix

Dr. Jeffrey S. Ross is a Professor of Radiology at the Mayo Clinic College of Medicine, and practices neuroradiology at the Mayo Clinic in Phoenix, Arizona. His publications include over 100 peer-reviewed articles, nearly 60 non-refereed articles, 33 book chapters, and 10 books. He was an AJNR Senior Editor from 2006-2015, is a member of the editorial board for 3 other journals, and a manuscript reviewer for 10 journals. He became Editor-in-Chief of the AJNR in July 2015. He received the Gold Medal Award from the ASSR in 2013.