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Research Paper|Volume 11, Issue 2|pp 673—696

IDH mutation-specific radiomic signature in lower-grade gliomas

Xing Liu1, Yiming Li1, Shaowu Li2, Xing Fan1, Zhiyan Sun1, Zhengyi Yang4, Kai Wang5, Zhong Zhang3, Tao Jiang1,3,6,7,8, Yong Liu4,9, Lei Wang3, Yinyan Wang3
  • 1Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
  • 2Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
  • 3Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 4Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  • 5Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  • 6Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
  • 7China National Clinical Research Center for Neurological Diseases, Beijing, China
  • 8Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA)
  • 9National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

* * Equal contribution

Received: November 12, 2018Accepted: January 6, 2019Published: January 29, 2019
https://doi.org/10.18632/aging.101769

Copyright: © 2019 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.