Parita Sanghani1, Ang Beng Ti2, Nicolas Kon Kam King2, and Hongliang Ren1
1Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore, 2Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
Synopsis
We evaluated 13 shape features of glioblastoma
multiforme (GBM) tumor for overall survival (OS) prognosis in 75 patients using
univariate and multivariate Cox regression analysis. Age and Karnofsky
performance scale were used as covariates for the multivariate analysis. Three
shape features were found to be significant for OS prognosis in GBM patients.
Kaplan-Merier survival curves were obtained for the significant features to
illustrate their effectiveness. In future works, these shape features can be
used along with volumetric and texture features derived from the tumor for OS
prediction of GBM patients.
Introduction
Age, Karnofsky performance scale (KPS), extent of
resection, and the degree of necrosis as well as enhancement of the tumor on
pre-operative MR image studies have been found to be prognostic of overall
survival (OS) in glioblastoma multiforme (GBM) patients1. In previous studies2, some shape features have also been found to be prognostic of OS in GBM patients. Shape features quantify various aspects of tumor surface irregularities (an irregular
tumor surface is associated with poor prognosis). In this work, we study the
effectiveness of 13 GBM tumor shape (3D and 2D) features for OS
prognosis of GBM patients. 12 out of 13 shape features used in this work have
been analyzed for OS prognosis for the first time.Method
FLAIR MR images of 75 patients obtained from the BraTS
2017 dataset were used in this study3,4. The shape features were
extracted from the FLAIR mask (consisting of enhancing tumor, necrosis, and
edema regions (refer Figure-1)).
3D tumor shape
features
1.
Bounding
ellipsoid volume ratio (BEVR):
BEVR2
is an indicator of the tumor shape irregularity. It is the ratio of the tumor
volume to the volume of the Minimum Volume Bounding Ellipsoid (MVBE) enclosing the
tumor5.
2.
MVBE
orientation:
Angles of rotation of
the major, intermediate and minor axes of the MVBE were computed from the
rotation matrix of the MVBE of the tumor. The rotation
matrix is obtained from the ellipsoid information matrix5.
3.
Spherical
Disproportion (SD) and Sphericity(SP):
SD
and SP are measures of the roundness of the tumor region. 1 <= SP
and SD <= 0. For a perfectly spherical object, SP, SD
= 1. These features were computed using the
Pyradiomics6 package.
2D tumor shape
features7
Mean Radial Distance (MRD), Radial Distance Standard
Deviation (RDSD), Mass Circularity (MC), Entropy of radial distance (Entropy),
Area Ratio (AR), Zero Crossing Count (ZC) and Mass Boundary Roughness (MRB)
were extracted from the largest axial slice of the FLAIR mask.
Statistical analysis
Univariate Cox regression analysis was performed for
all 2D and 3D shape features individually. The Hazard ratios (HR) and its 95%
confidence interval (CI), Wald statistic value (w), and p-value obtained, were used to determine whether a feature
is significant for GBM OS prognosis.
The shape features found to be significant from the
univariate analysis were analyzed using multivariate Cox regression with
patient age and KPS as covariates.
Kaplan-Meier (KM) curves were obtained for the features which were found to be significant from both univariate and multivariate Cox
regression analysis, in order to
illustrate their effectiveness in
OS prognosis of GBM patients. For each shape feature, two patient groups were generated based on the median value of the feature in consideration. Subsequently, each group’s
survival curve was observed.
Results and Discussion
The results of the univariate and multivariate Cox
regression analysis are shown in Figure-2 and Figure- 3 respectively. None of the 2D
shape features evaluated in this study were found to be significant for OS
prognosis in GBM patients. Mass circularity, entropy
of radial distance, and zero crossing count had p-values <0.05 (Figure-2).
However, the 95% CI of their HR included the value 1.0, which indicates that
the OS prognosis by the feature is poor.
Hence, these features were not considered to be significant. Bounding
Ellipsoid Volume Ratio, Spherical Disproportion and Sphericity had p-value <
0.05 and high Wald statistic value from both univariate and multivariate Cox
regression analysis (Figure 2 and 3). Figure-4 shows the KM survival curves for
the features found to be significant from both univariate and multivariate Cox
regression analysis. High BEVR and sphericity values indicate that the
tumor has less irregularities. Thus, GBM patients whose tumor masks result in BEVR
and sphericity value above the median value for the population are expected to
survive longer. In case of spherical
disproportion, the inverse should be true, as low spherical disproportion
indicates less irregularities. The observations from Figure 4 confirms this
hypothesis.Conclusion
This work assessed 2D and 3D shape features derived
from the FLAIR abnormality region. BEVR was
found to be
prognostic of OS
in GBM patients,
which is consistent with the findings by Czarnek2.
Of the 12
shape features being used in this context for the first time, sphericity and
spherical disproportion were found to be significant in OS prognosis of GBM
patients. In future works, we
aim to integrate these shape features with other features (such as texture,
clinical, volumetric etc.) to predict the OS of GBM patients.
Acknowledgements
This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NUSRI China Jiangsu Provincial Grant BK20150386 BE2016077 and NMRC Bedside Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren. This research is also supported by the Singapore Ministry of Health’s National Medical Research Council under its Translational and Clinical Research Flagship Program- Tier 1 (Project No: NMRC/TCR/016-NNI/2016).References
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