Somatic mutations associated with MRI-derived volumetric features in glioblastoma

• Type of Material: Article
• Authors: David Gutman, Emory University; William D. Dunn Jr, Emory University; Patrick Grossmann, Harvard University; Lee Cooper, Emory University; Chad Holder, Emory University; Keith L. Ligon, Harvard University; Brian M. Alexander, Harvard University; Hugo J. W. L. Aerts, Harvard University
• Language: English
• Date: 2015-12-01
• Publisher: Springer Verlag (Germany)
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2015
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1007/s00234-015-1576-7
• Title of Journal or Parent Work: Neuroradiology
• ISSN: 0028-3940
• Volume: 57
• Issue: 12
• Start Page: 1227
• End Page: 1237
• Grant/Funding Information: We acknowledge financial support from the National Institute of Health (NIH-USA U24CA194354, and NIH-USA U01CA190234).
• Keywords: THERAPY; SUBTYPES; HIGH-GRADE GLIOMAS; PATTERNS; Science & Technology; MRI; SURVIVAL; IDH1; Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging; GBM; Volumetrics; Radiogenomics; RESPONSE ASSESSMENT CRITERIA; EGFR; Life Sciences & Biomedicine; Clinical Neurology; IMAGES; INFORMATION; Imaging genomics; Neuroimaging
• Research Categories: Health Sciences, Radiology; Health Sciences, Oncology

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.