  Radiomics is an emerging area, which presents a large set of computational
methods and techniques to extract quantitative characteristics from magnetic
resonance images. In the feature extraction stage, its outputs must be well
defined and carefully evaluated, to provide imaging diagnostics, prognoses and
responses to treatment therapies. In this study, we present the extraction of
quantitative characteristics from magnetic resonance images in low-grade
gliomas using the Pyradiomics library and, using a multilayer perceptron neural
network, we will show the prediction of the deletion of the 1p / 19q
chromosomes in these gliomas. Several studies show that 1p / 19q chromosomal
codelection is a positive prognostic factor in low-grade gliomas, as they are
more sensitive to chemotherapy. Due to the large number of extracted
characteristics, it was necessary to use a dimensionality reduction technique,
the analysis principal components, which proved to be efficient in this study.
After training and testing the characteristics performed by the multilayer
perceptron neural network, the results showed to be very promising in detecting
the deletion status of chromosomes 1p / 19q, mainly taking into account the
possibility of avoiding surgical biopsies for this diagnosis.
