This manuscript correlates patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests.

Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBV NER ), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBV NER  and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBV NER  marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBV NER  as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBV NER , age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Conclusion Patients with high rCBV NER  and NER crossing the midline and those with high rCBV NER  and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBV NER  provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Data Access

Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever

Data Type Download all or Query/Filter
Image Data (DICOM)

Clinical, Genomic, and Radiologist Assessments (XLS)

Please contact  with any questions regarding usage.

Detailed Description

For more information about the authors' perfusion analysis and the full TCGA-GBM data sets please see the following links:

Citations & Data Usage Policy 

Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J, Freymann J, Jaffe CC, Mikkelsen T, and Flanders A. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. The Cancer Imaging Archive.

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Radiology. 2014 Mar 19:131691. [Epub ahead of print] Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. PMID:24646147

Other Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 1 (Current): 2014/07/24

Data Type Download all or Query/Filter
Image Data (DICOM)

Supplemental Data (XLS)