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.