A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma

J Neurosurg. 2016 Apr;124(4):1008-17. doi: 10.3171/2015.4.JNS142732. Epub 2015 Oct 16.


Objective: Individual MRI characteristics (e.g., volume) are routinely used to identify survival-associated phenotypes for glioblastoma (GBM). This study investigated whether combinations of MRI features can also stratify survival. Furthermore, the molecular differences between phenotype-induced groups were investigated.

Methods: Ninety-two patients with imaging, molecular, and survival data from the TCGA (The Cancer Genome Atlas)-GBM collection were included in this study. For combinatorial phenotype analysis, hierarchical clustering was used. Groups were defined based on a cutpoint obtained via tree-based partitioning. Furthermore, differential expression analysis of microRNA (miRNA) and mRNA expression data was performed using GenePattern Suite. Functional analysis of the resulting genes and miRNAs was performed using Ingenuity Pathway Analysis. Pathway analysis was performed using Gene Set Enrichment Analysis.

Results: Clustering analysis reveals that image-based grouping of the patients is driven by 3 features: volume-class, hemorrhage, and T1/FLAIR-envelope ratio. A combination of these features stratifies survival in a statistically significant manner. A cutpoint analysis yields a significant survival difference in the training set (median survival difference: 12 months, p = 0.004) as well as a validation set (p = 0.0001). Specifically, a low value for any of these 3 features indicates favorable survival characteristics. Differential expression analysis between cutpoint-induced groups suggests that several immune-associated (natural killer cell activity, T-cell lymphocyte differentiation) and metabolism-associated (mitochondrial activity, oxidative phosphorylation) pathways underlie the transition of this phenotype. Integrating data for mRNA and miRNA suggests the roles of several genes regulating proliferation and invasion.

Conclusions: A 3-way combination of MRI phenotypes may be capable of stratifying survival in GBM. Examination of molecular processes associated with groups created by this combinatorial phenotype suggests the role of biological processes associated with growth and invasion characteristics.

Keywords: AUC = area under the curve; EMT = epithelial-to-mesenchymal transition; FDR = false discovery rate; GBM = glioblastoma; GSEA = Gene Set Enrichment Analysis; IPA = Ingenuity Pathway Analysis; OXPHOS = oxidative phosphorylation; PFS = progression-free survival; ROC = receiver operating characteristic; TCGA = The Cancer Genome Atlas; TCIA = The Cancer Imaging Archive; VASARI = Visually AcceSAble Rembrandt Images; clustering; combinatorial phenotype analysis; differential expression analysis; glioblastoma; imaging genomics; miRNA = microRNA; oncology; radiogenomics.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / genetics
  • Brain Neoplasms / surgery
  • Cluster Analysis
  • Endpoint Determination
  • Female
  • Glioblastoma / diagnostic imaging*
  • Glioblastoma / genetics
  • Glioblastoma / surgery
  • Humans
  • Male
  • MicroRNAs / biosynthesis
  • MicroRNAs / genetics
  • Molecular Imaging
  • Multimodal Imaging
  • Neoplasm Invasiveness
  • Phenotype
  • Predictive Value of Tests
  • Radiography
  • Survival Analysis
  • Treatment Outcome


  • MicroRNAs