Somatic mutations associated with MRI-derived volumetric features in glioblastoma.
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...
| Published in: | Neuroradiology Vol. 57; no. 12; pp. 1227 - 1238 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2015
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111004654&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111004654 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2015 vid: 57 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111004654 111004654 111004654 10.1007/s00234-015-1576-7 NLM26337765 111004654 ppf: 1227 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Somatic mutations associated with MRI-derived volumetric features in glioblastoma. aug: au: Gutman, David Dunn, William Grossmann, Patrick Cooper, Lee Holder, Chad Ligon, Keith Alexander, Brian Aerts, Hugo affil: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston USA sug: subj: Mutation Magnetic Resonance Imaging Evaluation Glioma Pathology Glioma Familial and Genetic Genomics Necrosis Edema Human Male Female Glioma Diagnosis Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|