Quantitative radiomic profiling of glioblastoma represents transcriptomic expression
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Doo-Sik Kong1,2,3,*, Junhyung Kim2,*, Gyuha Ryu3,*, Hye-Jin You2,3, Joon Kyung Sung4, Yong Hee Han5, Hye-Mi Shin2,3, In-Hee Lee2, Sung-Tae Kim6, Chul-Kee Park7, Seung Hong Choi8, Jeong Won Choi1, Ho Jun Seol1, Jung-Il Lee1 and Do-Hyun Nam1,2,3
1Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
2Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea
3Department of Health Science and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea
4Department of Computer Science, Korea University, Seoul, Republic of Korea
5Medical System Research Department, Convergence Technology Institute, Hyundai Heavy Industries, Co., Ltd, Ulsan, Republic of Korea
6Department of Radiology, Samsung Medical Center, Seoul, Republic of Korea
7Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea
8Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
*These authors have contributed equally to this work
Do-Hyun Nam, email: [email protected]
Keywords: quantitative imaging; glioblastoma; radiomic; classification; phenotypes
Received: January 31, 2017 Accepted: September 06, 2017 Published: January 05, 2018
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.
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