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.
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 firstname.lastname@example.org with any questions regarding usage.
Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:
For more information about the authors' perfusion analysis and the full TCGA-GBM data sets please see the following links:
Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:
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. https://doi.org/10.7937/K9/TCIA.2014.FAB7YRPZ
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research: