Radiogenomics of glioblastoma

• Verfasst von: Vollmuth, Philipp [VerfasserIn]
• Verfasst von: Reinhardt, Annekathrin [VerfasserIn]
• Verfasst von: Burth, Sina [VerfasserIn]
• Verfasst von: Wick, Antje [VerfasserIn]
• Verfasst von: Eidel, Oliver [VerfasserIn]
• Verfasst von: Debus, Jürgen [VerfasserIn]
• Verfasst von: Herold-Mende, Christel [VerfasserIn]
• Verfasst von: Unterberg, Andreas [VerfasserIn]
• Verfasst von: Pfister, Stefan [VerfasserIn]
• Verfasst von: Wick, Wolfgang [VerfasserIn]
• Verfasst von: Deimling, Andreas von [VerfasserIn]
• Verfasst von: Bendszus, Martin [VerfasserIn]
• Verfasst von: Capper, David [VerfasserIn]
• Titel: Radiogenomics of glioblastoma
• Titelzusatz: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features
• Verf.angabe: Philipp Kickingereder, David Bonekamp, Martha Nowosielski, Annekathrin Kratz, Martin Sill, Sina Burth, Antje Wick, Oliver Eidel, Heinz-Peter Schlemmer, Alexander Radbruch, Jürgen Debus, Christel Herold-Mende, Andreas Unterberg, David Jones, Stefan Pfister, Wolfgang Wick, Andreas von Deimling, Martin Bendszus, David Capper
• E-Jahr: 2016
• Jahr: 16 August 2016
• Umfang: 12 S.
• Fussnoten: Gesehen am 22.09.2017
• Titel Quelle: Enthalten in: Radiology
• Ort Quelle: Oak Brook, Ill. : Soc., 1923
• Jahr Quelle: 2016
• Band/Heft Quelle: 281(2016), 3, Seite 907-918
• ISSN Quelle: 1527-1315
• DOI: doi:10.1148/radiol.2016161382
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1563737434

Abstract

PurposeTo evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma.Materials and MethodsRetrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I “PGFRA,” RTK II “classic”), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics.ResultsThere was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant.ConclusionThe authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma.© RSNA, 2016Online supplemental material is available for this article.