The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
Open accessAbstract
PURPOSE: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival. MATERIALS AND METHODS: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test. RESULTS: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01). CONCLUSION: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.
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Cited by
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
Self-citationThe Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review
Aerts, H. J. W. L. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2016.2631 · Cited by 669
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662
Self-citationMazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
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. (2014) · Radiology · doi:10.1148/radiol.14131731 · Cited by 319
Metadata
- Authors
- Gutman, D. A., Cooper, L. A. D., Hwang, S. N., Holder, C. A., Gao, J., Aurora, T. D., Dunn, W. D., Scarpace, L., Mikkelsen, T., Jain, R., Wintermark, M., Jilwan, M., Raghavan, P., Huang, E., Clifford, R. J., Mongkolwat, P., Kleper, V., Freymann, J., Kirby, J., Zinn, P. O., Moreno, C. S., Jaffe, C., Colen, R., Rubin, D. L., Saltz, J., Flanders, A., & Brat, D. J.
- Venue
- Radiology
- Published
- 2013-05
- Volume
- 267
- Issue
- 2
- Pages
- 560-569
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2098812986
- Cited by
- 420
- References
- 21
- FWCI
- 16.12
- Type
- journal article
- Language
- en
- Open access
- green
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- Gutman, D. A., Cooper, L. A. D., Hwang, S. N., Holder, C. A., Gao, J., Aurora, T. D., Dunn, W. D., Scarpace, L., Mikkelsen, T., Jain, R., Wintermark, M., Jilwan, M., Raghavan, P., Huang, E., Clifford, R. J., Mongkolwat, P., Kleper, V., Freymann, J., Kirby, J., . . . Brat, D. J. (2013). MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. Radiology. https://doi.org/10.1148/radiol.13120118
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