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tumor, imaging, features, pubmed, article, google, scholar, volumes, volume, volumetric, glioblastoma, tumors, contrast, mutations, significantly, enhancing, patients, images, necrosis, auc, analysis, cancer, mutation, results, status, image, data, total, tflair, mri, bulk, central, somatic, gbm, fig, hyperintensity, cas, size, tweighted, clinical, egfr, smaller, wildtype, molecular, survival, flair, predicted, genes, feature, significant,

Topics {✒️}

/web/packages/dplr/vignettes/timeseries-dplr t1-weighted post-gd contrast t2-flair hyperintensity/total tumor t2-weighted flair image smaller t2-flair hyperintensity magnetic resonance imaging org/package=cgdsr jenkinson t2-flair hyperintensity volume t2-weighted flair images t2-flair hyperintensity volumes full size image article download pdf gov/action/collaborations/vasari/ t2-flair mr images dana-farber cancer institute low-dimensional feature extraction semi-automatic volumetric algorithms contrast enhancing/tumor bulk contrast enhancing/total tumor necrosis/contrast enhancing ratio mri-derived volumetric features left y-axis corresponds t2-flair hyperintensity drug-targetable dna mutations gene specifically cancer genome atlas neuro-oncology working group egfr-mutated tumors showed full access tumor size based highly necrosis/low ce enable open science vasari research project van delft fw imaging genomic mapping privacy choices/manage cookies preoperative t1-contrast wild-type tumors zinn po high ce/low necrosis somatic mutation status somatic mutation patterns wild-type tumor imaging genomic analyses related subjects supplemental digital content malignant brain tumors vasari feature set imaging feature volumes brain tumors characteristics

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         headline:Somatic mutations associated with MRI-derived volumetric features in glioblastoma
         description: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). 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. 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. 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.
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         MRI
         Imaging genomics
         Volumetrics
         Neuroradiology
         Imaging / Radiology
         Neurology
         Neurosurgery
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