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Oncotarget. 2018 Jan 5;9(5):6336-6345. doi: 10.18632/oncotarget.23975. eCollection 2018 Jan 19.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

Author information

1
Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
2
Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea.
3
Department of Health Science and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
4
Department of Computer Science, Korea University, Seoul, Republic of Korea.
5
Medical System Research Department, Convergence Technology Institute, Hyundai Heavy Industries, Co., Ltd, Ulsan, Republic of Korea.
6
Department of Radiology, Samsung Medical Center, Seoul, Republic of Korea.
7
Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea.
8
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.

Abstract

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

KEYWORDS:

classification; glioblastoma; phenotypes; quantitative imaging; radiomic

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest.

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