  Glioma grading before surgery is very critical for the prognosis prediction
and treatment plan making. We present a novel wavelet scattering-based radiomic
method to predict noninvasively and accurately the glioma grades. The method
consists of wavelet scattering feature extraction, dimensionality reduction,
and glioma grade prediction. The dimensionality reduction was achieved using
partial least squares (PLS) regression and the glioma grade prediction using
support vector machine (SVM), logistic regression (LR) and random forest (RF).
The prediction obtained on multimodal magnetic resonance images of 285 patients
with well-labeled intratumoral and peritumoral regions showed that the area
under the receiver operating characteristic curve (AUC) of glioma grade
prediction was increased up to 0.99 when considering both intratumoral and
peritumoral features in multimodal images, which represents an increase of
about 13% compared to traditional radiomics. In addition, the features
extracted from peritumoral regions further increase the accuracy of glioma
grading.
