Predição da codeleção 1p/19q utilizando atributos radiômicos e aprendizado de máquina

In this work, we demonstrate the prediction of co-deletion of chromosome 1p/19q in low-grade gliomas, as it represents a positive prognostic factor for chemotherapy treatment. From the analysis of magnetic resonance images, we use machine learning based on radiomics features extracted from the images using the PyRadiomics library. The classification was performed using two models of classification algorithms: logistic regression (RL) and random forest (RF). We use the principal component analysis to reduce the dimensionality of the features. After the execution of the RL and RF algorithms in the training set, the generated models were validated in the validation set, obtaining 83.24% and 84.67% accuracy, respectively. These results showed to be very promising in the prediction of the co-deletion status of chromosome 1p/19q.