Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
Open accessAbstract
Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS ( p = 0.003).
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Seldom were machine learning investigations on prospective data.
“Seldom were machine learning investigations on prospective data, an issue endemic in the field84.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [84]
The Methods (Study Population) describe this machine-learning study as a retrospective analysis, and the Discussion identifies retrospective data collection as a limitation. That supports one example of non-prospective research, but the paper does not establish that prospective machine-learning investigations are seldom conducted across the field. (LLM, conf=0.78)
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References 47 · Cited by 157
- Authors
- Beig, N., Patel, J., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., Ahluwalia, M., Madabhushi, A., & Tiwari, P.
- Affiliations
- Case Western Reserve University (US)
- Cleveland Clinic (US)
- University Hospitals of Cleveland (US)
- Corresponding author
- Beig, Niha
- Tiwari, Pallavi
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-01-08
- Volume
- 8
- Issue
- 1
- Pages
- 7-7
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5758516
- OpenAlex
- W2782510979
- Cited by
- 157
- References
- 47
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 18 · 2023: 18 · 2022: 17 · 2021: 25 · 2020: 29 · 2019: 18 · 2018: 9
- FWCI
- 13.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Defense (W81XWH-16, W81XWH, PC120857, LC130463, W81XWH1610329)
- Wallace H. Coulter Foundation
- Case Western Reserve University (1U24CA199374-01, 1 C06 RR12463-01, C06 RR12463, R01CA208236-01A1, W81XWH-16-1-0329)
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health (W81XWH)
- National Cancer Institute (PC120857, 1U24CA199374-01, R01CA202752-01A1 R01CA208236-01A1 R21CA179327-01; R21CA195152-01, U24CA199374, R01CA202752-01A1 R01CA208236-01A1 R21CA179327-01, R01CA208236, R01CA202752, R21CA195152, R21CA179327, W81XWH-16, T32CA094186)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098503)
- National Center for Research Resources (1 C06 RR12463, C06RR012463, C06RR12463, 1C06RR12463‐01, C06 RR 12463-01)
- DOD Peer Reviewed Cancer Research Program (W81XWH-16–1-0329, 1-0329, W81XWH-16)
- 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, tumor hypoxia, radiogenomics, overall survival prediction, T2-weighted MRI, FLAIR MRI, tumor heterogeneity
- MeSH terms
- Adult, Aged, Hypoxia, Brain Neoplasms, Female, Glioblastoma, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prognosis, Signal Transduction, Biomarkers, Proportional Hazards Models, Gene Expression Profiling, Genomics, Kaplan-Meier Estimate
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