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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648958/ https://www.ncbi.nlm.nih.gov/pubmed/26337765 http://dx.doi.org/10.1007/s00234-015-1576-7 |
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author | Gutman, David A. Dunn, William D. Grossmann, Patrick Cooper, Lee A. D. Holder, Chad A. Ligon, Keith L. Alexander, Brian M. Aerts, Hugo J. W. L. |
author_facet | Gutman, David A. Dunn, William D. Grossmann, Patrick Cooper, Lee A. D. Holder, Chad A. Ligon, Keith L. Alexander, Brian M. Aerts, Hugo J. W. L. |
author_sort | Gutman, David A. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00234-015-1576-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4648958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-46489582015-11-24 Somatic mutations associated with MRI-derived volumetric features in glioblastoma Gutman, David A. Dunn, William D. Grossmann, Patrick Cooper, Lee A. D. Holder, Chad A. Ligon, Keith L. Alexander, Brian M. Aerts, Hugo J. W. L. Neuroradiology Diagnostic Neuroradiology 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00234-015-1576-7) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2015-09-04 2015 /pmc/articles/PMC4648958/ /pubmed/26337765 http://dx.doi.org/10.1007/s00234-015-1576-7 Text en © The Author(s) 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Diagnostic Neuroradiology Gutman, David A. Dunn, William D. Grossmann, Patrick Cooper, Lee A. D. Holder, Chad A. Ligon, Keith L. Alexander, Brian M. Aerts, Hugo J. W. L. Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title | Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title_full | Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title_fullStr | Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title_full_unstemmed | Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title_short | Somatic mutations associated with MRI-derived volumetric features in glioblastoma |
title_sort | somatic mutations associated with mri-derived volumetric features in glioblastoma |
topic | Diagnostic Neuroradiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648958/ https://www.ncbi.nlm.nih.gov/pubmed/26337765 http://dx.doi.org/10.1007/s00234-015-1576-7 |
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