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Neuroradiology. 2015 Dec;57(12):1227-37. doi: 10.1007/s00234-015-1576-7. Epub 2015 Sep 4.

Somatic mutations associated with MRI-derived volumetric features in glioblastoma.

Author information

1
Departments of Neurology, Emory University School of Medicine, Atlanta, GA, USA. dagutman@gmail.com.
2
Biomedical Informatics, Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA. dagutman@gmail.com.
3
Departments of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
4
Biomedical Informatics, Emory University School of Medicine, 1648 Pierce Dr NE, Atlanta, GA, 30307, USA.
5
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
6
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
7
Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
8
Pathology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
9
Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Abstract

INTRODUCTION:

MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM).

METHODS:

Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status.

RESULTS:

Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature.

CONCLUSION:

MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.

KEYWORDS:

GBM; Imaging genomics; MRI; Radiogenomics; Volumetrics

PMID:
26337765
PMCID:
PMC4648958
DOI:
10.1007/s00234-015-1576-7
[Indexed for MEDLINE]
Free PMC Article

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