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.
Highlights
Glioblastoma (GBM) is the most common and fatal type of glioma, accounting for 40% of all malignant primary brain tumors and showing a median survival of 14 months despite treatment by surgical resection followed by concurrent chemoradiotherapy
Using a high-throughput approach, we identified radiomic signatures consisting of 82 magnetic resonance (MR) imaging features that segregated patients with GBM into three distinct consensus clusters (Figure 1A)
For each of the 82 radiomic phenotypes, we investigated the Gene Set Enrichment Analysis (GSEA) for identifying the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways whose transcriptional changes associated with the change of tumor radiomic phenotype
Summary
Glioblastoma (GBM) is the most common and fatal type of glioma, accounting for 40% of all malignant primary brain tumors and showing a median survival of 14 months despite treatment by surgical resection followed by concurrent chemoradiotherapy. This dismal prognosis is largely attributed to the cancer heterogeneity of GBM [1]. In radiographic studies of glioblastoma, physiologic imaging biomarkers are gradually being uncovered and are believed to be crucial for predicting the treatment response of the tumor or natural course of tumor progression
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