Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
Open accessAbstract
PURPOSE: To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data. MATERIALS AND METHODS: Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways. RESULTS: Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes. CONCLUSION: Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.
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In Gevaert et al, three of 54 agnostic features were related to survival.
“three of 54 were related to survival”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [57]
In Gevaert et al, seven agnostic features were related to gene expression.
“and seven were related to gene expression.”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [57]
Gevaert et al extracted a large number of semantic and agnostic features in 55 patients with glioma who had undergone gene expression profiling.
“In a more recent study, Gevaert et al extracted a large number of semantic and agnostic features in 55 patients with glioma who had undergone gene expression profiling (57).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [57]
In Gevaert et al, when gene expression was assessed via pathways, approximately half of the imaging features showed strong correlation to genomics.
“When gene expression was assessed via pathways, approximately half of the imaging features showed strong correlation to genomics.”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [57]
In Gevaert et al, most agnostic features could be correlated with semantic features.
“Of the agnostic features, most could be correlated with the semantic features;”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [57]
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- Authors
- Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K.
- Affiliations
- Stanford Medicine (US)
- ORCID
- Venue
- Radiology
- Published
- 2014-10
- Volume
- 273
- Issue
- 1
- Pages
- 168-174
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC4725327
- OpenAlex
- W2111389142
- Cited by
- 319
- References
- 26
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 17 · 2023: 17 · 2022: 15 · 2021: 36 · 2020: 45 · 2019: 39 · 2018: 38 · 2017: 36 · 2016: 41 · 2015: 17 · 2014: 1 · 2013: 1
- FWCI
- 24.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (U54CA149145, R01CA160251)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- glioblastoma, radiogenomics, quantitative imaging features, magnetic resonance imaging, gene modules, molecular subtypes, survival prediction, peritumoral edema, Vasari, non-invasive tumor characterization
- MeSH terms
- Algorithms, Brain Neoplasms, Contrast Media, Female, Glioblastoma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Necrosis, Phenotype, Gene Expression Regulation, Neoplastic, Survival Rate
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