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. 2014 Aug;272(2):484-93.
doi: 10.1148/radiol.14131691. Epub 2014 Mar 19.

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor

Rajan Jain  1 , Laila M Poisson, David Gutman, Lisa Scarpace, Scott N Hwang, Chad A Holder, Max Wintermark, Arvind Rao, Rivka R Colen, Justin Kirby, John Freymann, C Carl Jaffe, Tom Mikkelsen, Adam Flanders
Affiliations

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor

Rajan Jain et al. Radiology. 2014 Aug.

Abstract

Purpose: To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers.

Materials and methods: An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. 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.

Results: Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), 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 rCBVNER 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 rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, 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 rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

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Figures

Figure 1a:
Figure 1a:
Graphs of survival estimates demonstrate that rCBVNER is a significant predictor of (a) OS (log-rank test, P = .0103) and (b) PFS (log-rank test, P = .0223). rCBVNER is dichotomized at the median observed value.
Figure 1b:
Figure 1b:
Graphs of survival estimates demonstrate that rCBVNER is a significant predictor of (a) OS (log-rank test, P = .0103) and (b) PFS (log-rank test, P = .0223). rCBVNER is dichotomized at the median observed value.
Figure 2:
Figure 2:
Receiver operating characteristic curves are shown for rCBVNER (dichotomous, likelihood ratio test, P = .0122; AUC = 0.61), extent of resection (GTR or subtotal resection, likelihood ratio test, P = .002; AUC = 0.62), and the joint model (likelihood ratio test, P = .0006; AUC = 0.69) (n = 30). AUC was determined for 1-year survival. Sensitivity and specificity for the single dichotomous variable models can be read at the bend in the receiver operating characteristic curve.
Figure 3a:
Figure 3a:
Graphs depict survival classification after random survival forest ranking of potential predictors. (a) A representative tree to consider only rCBV and VASARI features caused rCBVNER to be selected first and then the T1/FLAIR ratio to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0165). (b) Allowing NER crossing of the midline to define the split of the high-rCBVNER subset also provides a significant separation of survival curves (Kaplan-Meier curves, log-rank test, P = .0067). (c) A representative tree to consider rCBV, VASARI features, and preoperative clinical parameters caused rCBVNER to be selected first and then KPS to split the low-rCBVNER subset and age to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0003).
Figure 3b:
Figure 3b:
Graphs depict survival classification after random survival forest ranking of potential predictors. (a) A representative tree to consider only rCBV and VASARI features caused rCBVNER to be selected first and then the T1/FLAIR ratio to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0165). (b) Allowing NER crossing of the midline to define the split of the high-rCBVNER subset also provides a significant separation of survival curves (Kaplan-Meier curves, log-rank test, P = .0067). (c) A representative tree to consider rCBV, VASARI features, and preoperative clinical parameters caused rCBVNER to be selected first and then KPS to split the low-rCBVNER subset and age to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0003).
Figure 3c:
Figure 3c:
Graphs depict survival classification after random survival forest ranking of potential predictors. (a) A representative tree to consider only rCBV and VASARI features caused rCBVNER to be selected first and then the T1/FLAIR ratio to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0165). (b) Allowing NER crossing of the midline to define the split of the high-rCBVNER subset also provides a significant separation of survival curves (Kaplan-Meier curves, log-rank test, P = .0067). (c) A representative tree to consider rCBV, VASARI features, and preoperative clinical parameters caused rCBVNER to be selected first and then KPS to split the low-rCBVNER subset and age to split the high-rCBVNER subset (Kaplan-Meier curves, log-rank test, P = .0003).
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