  Objectives: Glioblastomas are the most aggressive brain and central nervous
system (CNS) tumors with poor prognosis in adults. The purpose of this study is
to develop a machine-learning based classification method using radio-mic
features of multi-parametric MRI to classify high-grade gliomas (HGG) and
low-grade gliomas (LGG). Methods: Multi-parametric MRI of 80 patients, 40 HGG
and 40 LGG, with gliomas from the MICCAI BRATs 2015 training database were used
in this study. Each patient's T1, contrast-enhanced T1, T2, and Fluid
Attenuated Inversion Recovery (FLAIR) MRIs as well as the tumor contours were
provided in the database. Using the given contours, radiomic features from all
four multi-parametric MRIs were extracted. Of these features, a feature
selection process using two-sample T-test and least absolute shrinkage,
selection operator (LASSO), and a feature correlation threshold was applied to
various combinations of T1, contrast-enhanced T1, T2, and FLAIR MRIs
separately. These selected features were then used to train, test, and
cross-validate a random forest to differentiate HGG and LGG. Finally, the
classification accuracy and area under the curve (AUC) were used to evaluate
the classification method. Results: Optimized parameters showed that on
average, the overall accuracy of our classification method was 0.913 or 73 out
of 80 correct classifications, 36/40 for HGG and 37/40 for LGG, with an AUC of
0.956 based on the combination with FLAIR, T1, T1c and T2 MRIs. Conclusion:
This study shows that radio-mic features derived from multi-parametric MRI
could be used to accurately classify high and lower grade gliomas. The
radio-mic features from multi-parametric MRI in combination with even more
advanced machine learning methods may further elucidate the underlying tumor
biology and response to therapy.
