4.7 Article

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

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SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-017-18310-0

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资金

  1. National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R01CA202752-01A1 R01CA208236-01A1 R21CA179327-01]
  2. National Institute of Diabetes and Digestive and Kidney Diseases [R21CA195152-01, R01DK098503-02]
  3. National Center for Research Resources [1 C06 RR12463-01]
  4. DOD Prostate Cancer Synergistic Idea Development Award [PC120857]
  5. DOD Lung Cancer Idea Development New Investigator Award [LC130463]
  6. DOD Prostate Cancer Idea Development Award
  7. DOD Peer Reviewed Cancer Research Program [W81XWH-16-1-0329]
  8. Ohio Third Frontier Technology Validation Fund
  9. Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering
  10. Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University

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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 naive 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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