Quantitative radiomic profiling of glioblastoma represents transcriptomic expression

Oncotarget
Doo-Sik KongDo-Hyun Nam

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 different...Continue Reading

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Citations

May 21, 2020·The Journal of International Medical Research·Xiaojun SunJian Zhou
Aug 29, 2019·Journal of Magnetic Resonance Imaging : JMRI·Anahita Fathi KazerooniChristos Davatzikos
Jun 6, 2019·Frontiers in Oncology·Ahmad ChaddadBassam Abdulkarim
May 1, 2021·Radiologic Clinics of North America·Chaitra Badve, Sangam Kanekar
Oct 16, 2021·AJNR. American Journal of Neuroradiology·M AkS Khatua

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Methods Mentioned

BETA
surgical resection
biopsy
biopsies
exome sequencing

Software Mentioned

GSNAP
nordicICE
Wheeler Aligner
BraTumIA
ClaNC
Burrows
Genome Analysis ToolKit ( GATK
SAMtools
ngCGH
Gene Set Enrichment Analysis ( GSEA )

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