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Prediction of Glioblastoma Multiforme
Patient Survival Using MR Image
Features and Gene Expression
Control/Tracking Number: 11-O-1519-ASNR
Nicolasjilwan, M.1·Clifford, R.2·Raghavan, P.1·Wintermark,
M.1·Hammoud, D.3·Huang, E.4·Jaffe, C.5·Freymann,
J.2·Kirby, J.2·Buetow, K.4·Huang, S.6·Holder, C.6·Gutman,
D.6·Flanders, A. E.7
1University of Virginia, Charlottesville, VA, 2SAIC-Frederick, Inc., Frederick, MD,
3National Institute of Health, Bethesda, MD, 4National Cancer Institute, Bethesda,
MD, 5Boston University School of Medicine, Boston, MA, 6Emory University
Hospital, Atlanta, GA, 7Thomas Jefferson University Hospital, Philadelphia, PA.
DISCLOSURE
Nothing to disclose
PURPOSE
 Utilize conventional MRI imaging features
to predict survival of patients with
glioblastoma multiforme (GBM) after initial
diagnosis.
 Linear regression models incorporating MR
imaging features and tumor gene
expression to predict patient survival.
 Important role in selecting treatment
options.
Materials & Methods
 The study is part of The Cancer Genome Atlas
(TCGA) MR imaging (MRI) characterization
project of the National Cancer Institute.
 MR images for 70 GBM patients made available
through the National Biomedical Imaging Archive
were reviewed independently by six
neuroradiologists.
 The VASARI feature scoring system for human
gliomas, developed at Thomas Jefferson
University Hospital, was employed.
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30 features clustered by categories.
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Lesion Location
Morphology of Lesion Substance
Morphology of Lesion Margin
Alterations in Vicinity of Lesion
Extent of resection
 620 genes associated with angiogenesis used in
this investigation.
 Survival was recoded as a binary categorical
variable: survival less than or greater than 1
year.
 Associations between imaging features and
survival were assessed using linear regression
models. Survival was the outcome; imaging
features were the predictors.
Well marginated Non-enhancing
F4
Enhancement Quality:
1=None 2=Mild/Minimal 3=Marked/Avid
F13 Definition of the non-enhancing margin
1= n/a 2= Smooth 3= Irregular
Courtesy Dr Adam Flanders
Predominantly Non-enhancing
F5 Proportion Enhancing:
1= n/a 2=None (0%) 3= <5% 4= 6-33% 5= 34-67%
6= 68-95% 7= >95% 8=All (100%)
Courtesy Dr Adam Flanders
RESULTS
Univariate Analysis of Association
between VASARI features and survival
 Individually, 6 MRI features show association to survival
with an unadjusted p-value < 0.05.
 Negative correlation with survival:
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Ependymal extension (F19), (P = 0.0012)
Longest dimension of lesion size (F29)
Deep white matter invasion (F21)
The presence of satellites (F24).
 Positive correlation with survival:
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Location of the tumor in the right (usually non-dominant)
hemisphere (F2).
Frontal lobe location (feature F1a), (P = 0.0098).
Linear regression models incorporating the most
significant VASARI feature, F19 (ependymal
extension), and expression of angiogenesis-related
genes.
 4 genes individually improve the predictive power of F19
(ependymal extension).
 Expression of ANG (angiogenin) and TGFB2 (TGF-
beta 2) genes negatively correlates with survival.
 CCL5 (chemokine (C-C motif) ligand 5) and TNF
(tumor necrosis factor) positively correlate with
survival.
 Feature F19 correctly predicts survival for 72% of the
cases. A model based on ependymal extension, CCL5,
ANG, TGFB2 and TNF correctly predicts survival for 82%
of patients.
CONCLUSION
 A subset of VASARI imaging features correlate well with
patient survival.
 Linear regression models incorporating multiple imaging
features or a single VASARI feature (ependymal
extension) and tumor gene expression can be used to
predict patient survival.
 We are refining these models and are investigating
whether including patient clinical characteristics into linear
models can improve their predictive power.
AKNOWLEDGMENT
University of Virginia
SAIC-Frederick
National Institute of Health
National Cancer Institute
Thomas Jefferson University Hospital
Emory University Hospital
Boston University School of Medicine