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Radiomics is an emerging area, which presents a large set of computationalmethods and techniques to extract quantitative characteristics from magneticresonance images. In the feature extraction stage, its outputs must be welldefined and carefully evaluated, to provide imaging diagnostics, prognoses andresponses to treatment therapies. In this study, we present the extraction ofquantitative characteristics from magnetic resonance images in low-gradegliomas using the Pyradiomics library and, using a multilayer perceptron neuralnetwork, we will show the prediction of the deletion of the 1p / 19qchromosomes in these gliomas. Several studies show that 1p / 19q chromosomalcodelection is a positive prognostic factor in low-grade gliomas, as they aremore sensitive to chemotherapy. Due to the large number of extractedcharacteristics, 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 multilayerperceptron neural network, the results showed to be very promising in detectingthe deletion status of chromosomes 1p / 19q, mainly taking into account thepossibility of avoiding surgical biopsies for this diagnosis.

Extra\c{c}\~ao e Classifica\c{c}\~ao de Caracter\'isticas Radi\^omicas em Gliomas de Baixo Grau para An\'alise da Codele\c{c}\~ao 1p/19q