Radiogenomics of Glioblastoma: Machine Learning–based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features

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.

Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

“…Previous studies have indicated that the visual morphology of brain tumors such as mass effect, contrast enhancement, and edema is related to the genetic phenotype of brain tumors . Moreover, correlation between DNA methylation status and MRI features has been reported, suggesting that noninvasive MGMT promoter methylation status detection is possible …”

“…26 Moreover, correlation between DNA methylation status and MRI features has been reported, suggesting that noninvasive MGMT promoter methylation status detection is possible. 27 Quantitative imaging features with clinical variables are broadly defined as radiomics. The metrics of feature data could be mined along with other variables including the genetic profile.…”

Radiogenomic Analysis of Oncological Data: A Technical Survey

“…In order to increase the use of non-invasive imaging as an emerging field of treatment response and personalized medicine, over the years, other works have been published in the field of the radiogenomics for the study of brain tumours, through the analysis of gene expression and DNA copy number variations in tumour tissues. Most of these are correlation studies that were performed using DNA microarray and MRI imaging modalities [125,126,129,131,132,133,139,141]. On the other hand, a plethora of studies investigated the possible predictive value of the radiogenomics approach, showing promising results [127,128,130,135,137,140,143].…”

Imaging biomarkers in oncology: Basics and application to MRI

“…Radiomic studies have used a number of techniques including statistical methods (histogram; gray‐level co‐occurrence matrix [GLCM]; gray‐level difference matrix [GLDM], run length matrix [RLM], gray level size zone matrix [GLSZM], and neighborhood gray tone difference matrix [NGTDM]) with or without Gaussian or Wavelet transformation; and fractal‐based methods across different sequences including T 2 ‐weighted, diffusion‐weighted, and DCE sequences. Initial radiogenomic studies including MRI have been performed in breast cancer renal cell carcinoma and glioma . Variable reproducibility has been shown across different classes of features and further validation work is still required for radiomic biomarkers.…”

“…Initial radiogenomic studies including MRI have been performed in breast cancer [108][109][110] renal cell carcinoma 111 and glioma. 112,113 Variable reproducibility has been shown across different classes of features 114 and further validation work is still required for radiomic biomarkers.…”