Radiogenomics and Imaging Phenotypes in Glioblastoma: Novel Observations and Correlation with Molecular Characteristics
Abstract
Radiogenomics is a provocative new area of research based on decades of previous work examining the association between radiological and histological features. Many generalized associations have been established linking anatomical imaging traits with underlying histopathology, including associations between contrast-enhancing tumor and vascular and tumor cell proliferation, hypointensity on pre-contrast T1-weighted images and necrotic tissue, and associations between hyperintensity on T2-weighted images and edema or nonenhancing tumor. Additionally, tumor location, tumor size, composition, and descriptive features tend to show significant associations with molecular and genomic factors, likely related to the cell of origin and growth characteristics. Additionally, physiologic MRI techniques also show interesting correlations with underlying histology and genomic programs, including associations with gene expression signatures and histological subtypes. Future studies extending beyond simple radiology-histology associations are warranted in order to establish radiogenomic analyses as tools for prospectively identifying patient subtypes that may benefit from specific therapies.
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Joint radiogenomic signatures were found to increase the predictive ability of generated models in glioma phenotype group.
“At the same time, hypothesis testing in glioma phenotype group verified the significant association between certain radiomic and genomic features with respect to overall and progression free survival, while joint radiogenomic signatures were found to increase the predictive ability of generated models.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [240]
Imaging features were linked to molecular GBM subtype classification (based on Verhaak and/or Philips classification) providing for non-invasive prognosis.
“Importantly, imaging features were linked to molecular GBM subtype classification (based on Verhaak and/ or Philips classification) providing for non-invasive prognosis [68], [240].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [240]
Glioma phenotype group hypothesis testing verified a significant association between certain radiomic and genomic features with respect to overall and progression free survival.
“At the same time, hypothesis testing in glioma phenotype group verified the significant association between certain radiomic and genomic features with respect to overall and progression free survival, while joint radiogenomic signatures were found to increase the predictive ability of generated models.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [240]
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Response criteria for phase II studies of supratentorial malignant glioma.
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Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
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- Authors
- Ellingson, B. M.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Ellingson, Benjamin M.
- Venue
- Current Neurology and Neuroscience Reports
- Published
- 2014-11-20
- Volume
- 15
- Issue
- 1
- Pages
- 506-506
- ISSN
- 1528-4042 (ISSN-L)
- 1534-6293
- OpenAlex
- W2150676817
- Cited by
- 132
- References
- 5
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 11 · 2023: 5 · 2022: 5 · 2021: 14 · 2020: 16 · 2019: 17 · 2018: 26 · 2017: 18 · 2016: 12
- FWCI
- 1.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Current Neurology and Neuroscience Reports (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- radiogenomics, glioblastoma, tumor cell proliferation, tumor necrosis, edema, gene expression signatures, histological subtypes, molecular subtypes, cell of origin
- MeSH terms
- Animals, Brain, Brain Neoplasms, Glioblastoma, Humans, Magnetic Resonance Imaging, Phenotype, Tumor Burden
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