Year: 2018
Type: article
Abstract: 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).
Source: Scientific Reports
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: U.S. Department of Defense, Wallace H. Coulter Foundation, Case Western Reserve University, Georgia Clinical and Translational Science Alliance, National Institutes of Health, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Research Resources, DOD Peer Reviewed Cancer Research Program
Awards: G1340616995, G1362410050, G1642746824, Comprehensive quantitative ultrafast 3D liver MRI, G21146293, G2115559220, Pathology Image Informatics Platform for visualization, analysis and management, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G3054115203, G3082257526, G3886026468, G4119534169, G4787017382, G5302883106, G5375817562, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, Computerized histologic image predictor of cancer outcome, G5721952423, G5850682332, G6239819367, G662883311, Computerized morphologic-molecular predictor of progression in DCIS, Histologic image-based aggressiveness prediction in p16+ oropharyngeal carcinoma, G7216376094, G761790451, MRI-PATHOLOGY CORRELATION FOR IMAGE ANALYTICS-BASED TREATMENT OUTCOME ASSESSMENT AND MARGIN PLANNING IN RECTAL CANCERS, G8188865956, G8610161792, Computational Genomic Epidemiology of Cancer (CoGEC) Training Program, G942918344
OpenAlex ID: W2782510979
