Anup Singh1,2, Neha Vats1, Virendra Kumar Yadav1, Anirban Sengupta3, Rakesh Kumar Gupta4, Sumeet Agarwal5, Mamta Gupta6, Rana Patir7, Sunita Ahlawat6, and Jitender Saini8
1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, 2Biomedical Engineering, AIIMS, New Delhi, India, 3Vanderbilt University Medical Center, Nashville, TN, United States, 4Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, 5Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India, 6Fortis Memorial Research Institute, Gurugram, India, 7Neurosurgery, Fortis Memorial Research Institute, Gurugram, India, 8Department of Neuroimaging and Interventional Radiology (NIIR), NIMHANS Bangalore, Bangalore, India
Synopsis
Imaging based diagnosis of Pilocytic Astrocytoma
(PA) is quite important for better
prognosis. PA can easily be misdiagnosed since its location, growth pattern,
and contrast enhancement often mimic a more aggressive high-grade glioma(HGG)
tumor. In the current study, quantitative analysis of T1-Perfusion(DCE) MRI
data was performed followed by extraction of various features from tumor region
and development of an optimized support-vector-machine(SVM) classifier for
automatic differentiation of PA vs HGG. The proposed machine learning based approach
which uses features derived from quantitative T1 perfusion MRI and tumor volume
fraction can enable accurate diagnosis of PA and HGG tumors.
Introduction
Pilocytic Astrocytoma(PA) are considered grade I
tumors in the current(2016) WHO classification of CNS tumors and have a
relatively good prognosis. PAs are usually well enhanced tumors, on
post-contrast T1-weighted MRI images, that resemble high-grade gliomas(HGGs). Some
of the PA show similar appearance to HGG on conventional MRI1-4. Due
to this, conventional imaging based diagnosis of PA has been less accurate.
Some of the recent studies have shown potential of quantitative maps of
perfusion MRI and diffusion MRI, particularly relative-cerebral-blood-volume(rCBV)
and apparent-diffusion-coefficient(ADC) in differentiation of PA from HGG. Maximum
rCVB show low values while ADC show high values in PA compared to HGG5-7.
A recently published study8, using a decision tree model based upon
radionics features derived from post-contrast T1-weighted images, have shown
accuracy of 86% in differentiation of PA from Glioblastoma, which is a grade IV
glioma. We hypothesis that a machine learning approach based upon quantitative
multi-parametric MRI features should differentiate PA from HGG with improved
accuracy. The current study included quantitative analysis of T1-perfusion MRI
data followed by extraction of various features from tumor region and development
of a support-vector-machine(SVM) classifier for automatic differentiation of PA
vs HGG. Material and Methods
This
retrospective study included 24 PA and 52 HGG (24 grade-III and 28 grade-IV)
patients, with tumor histopathological characterization done based on WHO
2016 guidelines. MRI was performed at 3T MR scanner (Philips Health Systems,
the Netherlands) using a 15 channel head coil. MRI data included conventional
T1-weighted(W), T2-W, fluid-attenuated-inversion-recovery(FLAIR), diffusion
weighted imaging, T1 mapping data, T1-Perfusion(DCE) MRI data and post-contrast
T1-weighted images covering entire tumor tissue. T1-Perfusion MRI data(40 time
points with temporal resolution of 3.9s) was acquired using 3D T1-TFE with 0.1
mmol/kg body-weight of GD-BOPTA contrast agent injected at 4th time
point.
Image processing and quantitative
analysis of MRI data were performed using in house developed software tools in
MATLAB. Image processing started with skull-removal and registration of
multi-parametric MRI data with T1 weighted images using SPM12 software9.
Quantitative analysis of T1-perfusion MRI was performed using leaky-tracer-kinetic-model
(LTKM)10 for computing volume-transfer-rate(Ktrans), volume-fraction-of-extracellular-extravascular-space(Ve),
volume-fraction-of-extracellular-extravascular-space(Vp), volume-transfer-rate-from-plasma-to-leakage-space
(λtrans); first-pass-analysis for
computing cerebral-blood-volume(CBV) and cerebral-blood-flow(CBF)11
and piecewise-linear model12 for computing slopes(wash-in and
wash-out rates), bolus-arrival-time(BAT) and time-to-peak(Beta) of contrast
agent. CBV was also corrected for leakage of contrast to obtain CBVcorr. The CBVcorr
and CBF maps were normalized with the mean value of a normal-appearing-white-matter(NAWM) mask to obtain rCBVcorr and rCBF.
Tumor region was
semi-automatically segmented into various components (enhancing, non-enhancing,
edema, necrotic/cyst) using a methodology reported in a recent study13.
Feature Vector for classification included quantitative parameters such as rCBVcorr
, rCBF, Ktrans, λtrans, Ve, Vp, BAT, BETA along with
volume fractions such as enhancing tumor(ET) volume, Edema volume, Necrotic
volume computed over tumor region. For quantitative parameters mean of
values>90th percentile in tumor region was used as feature.
Student’s t-test was
applied to evaluate statistical significance of all features in differentiation
of PA vs HGG. Receiver-operating-characteristic(ROC) analysis was performed for
each feature to find Area-under-curve(AUC) along with sensitivity and
specificity. SVM classification was done using both linear as well as radial
basis function (RBF) kernel in MATLAB(2018b). The classifier was optimized with
respect to its hyper parameters C and γ. A 10-fold Cross-Validation(CV) was
performed to estimate how well the classifier performs on unseen data. Random-Forest
classifier was used for feature optimization to be used in SVM classifier. ROC
analysis was performed for binary classification (PA vs HGG) based on the 10-fold
CV results.Results and Discussion
Figure-1
and Figure-2 show conventional MRI images and quantitative maps(rCBVcorr,
Ktrans, Slope-2) of a representative PA and HGG patient respectively. Both types
of tumor show similar contrast enhancement on conventional images. rCBVcorr,
Ktrans and Slope-2 show higher values in PA compared to HGG. Table-1 show average of some of the quantitative parameters over all patients
for PA and HGG tumors. It clearly shows higher values in PA for rCBF, Ktrans,
Slope-1, Slope-2, λtrans with statistically significant
differences(P<0.05) between PA vs HGG. In the current study, rCBV did not
show statistically significant difference between PA and HGG. Previous studies
have reported lower rCBV values, based upon DSC Perfusion MRI, in PA compared
to HGG. This might be due to the use of without leakage correction rCBV from
DSC MRI. Tumor volume fractions also show statistically significant
differences.
Linear SVM classifier based upon
optimum features provided a CV error of 3.4%. ROC analysis(Figure-3) based upon
optimized SVM classifier provided a sensitivity=98.04, specificity=91.67 and
AUC=99.08 in differentiation between PA vs HGG. ROC analysis results (AUC,
sensitivity and specificity) for individual parameters were much less than SVM
classification. The proposed approach has enabled accurate differentiation of
PA and HGG tumors; however, this approach needs to be evaluated on more number
of patients from multiple hospitals. Conclusion
Quantitative parameters, particularly Slope-1(wash-in
rate), Slope-2(washout rate), Ktrans, and λtrans, computed
from T1-Perfusion MRI data provided significant difference between PA and HGG. Volume
fractions were also significantly different between PA and HGG. Machine
learning classifier (SVM) based upon quantitative T1-Perfusion MRI features and
tumor volume fraction features enabled highly accurate(CV accuracy = 96.6%) differentiation
of PA and HGG tumors.Acknowledgements
This work was supported by IIT Delhi, Fortis Memorial Research Institute Gurugram and NIMHANS Bangalore. Authors acknowledge funding support from SERB DST Project nu YSS/2014/000092. Authors acknowledge Dr. Indrajit Saha from Philips India for technical support in data acquisition.References
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