TY - JOUR
T1 - MR imaging predictors of molecular profile and survival
T2 - Multi-institutional study of the TCGA glioblastoma data set
AU - Gutman, David A.
AU - Cooper, Lee A D
AU - Hwang, Scott N.
AU - Holder, Chad A.
AU - Gao, JingJing
AU - Aurora, Tarun D.
AU - Dunn, William D.
AU - Scarpace, Lisa
AU - Mikkelsen, Tom
AU - Jain, Rajan
AU - Wintermark, Max
AU - Jilwan, Manal
AU - Raghavan, Prashant
AU - Huang, Erich
AU - Clifford, Robert J.
AU - Mongkolwat, Pattanasak
AU - Kleper, Vladimir
AU - Freymann, John
AU - Kirby, Justin
AU - Zinn, Pascal O.
AU - Moreno, Carlos S.
AU - Jaffe, Carl
AU - Colen, Rivka
AU - Rubin, Daniel L.
AU - Saltz, Joel
AU - Flanders, Adam
AU - Brat, Daniel J.
PY - 2013/5
Y1 - 2013/5
N2 - 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 a 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.
AB - 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 a 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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U2 - 10.1148/radiol.13120118
DO - 10.1148/radiol.13120118
M3 - Article
C2 - 23392431
AN - SCOPUS:84876887800
SN - 0033-8419
VL - 267
SP - 560
EP - 569
JO - Radiology
JF - Radiology
IS - 2
ER -
