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Radiogenomic Analysis of Hypoxia Pathway is Predictive of Overall Survival in Glioblastoma

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Journal Sci Rep
Specialty Science
Date 2018 Jan 10
PMID 29311558
Citations 62
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Abstract

Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).

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References
1.
Gilbert M, Dignam J, Armstrong T, Wefel J, Blumenthal D, Vogelbaum M . A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med. 2014; 370(8):699-708. PMC: 4201043. DOI: 10.1056/NEJMoa1308573. View

2.
Zhang Z, Jiang H, Chen X, Bai J, Cui Y, Ren X . Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI. J Neurooncol. 2014; 119(1):207-14. DOI: 10.1007/s11060-014-1478-2. View

3.
Prasanna P, Tiwari P, Madabhushi A . Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep. 2016; 6:37241. PMC: 5118705. DOI: 10.1038/srep37241. View

4.
Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

5.
Diehn M, Nardini C, Wang D, McGovern S, Jayaraman M, Liang Y . Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A. 2008; 105(13):5213-8. PMC: 2278224. DOI: 10.1073/pnas.0801279105. View