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Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using
Philipp Kickingereder1, David Bonekamp1, Martha Nowosielski1
1From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.).
Abstract:
Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective 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. Results There 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. Conclusion The 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, 2016 Online supplemental material is available for this article.
