Application of MRI Radiomics Combined With Deep Learning Technology in Glioma Segmentation and Survival Prognosis

Accurate automatic segmentation and prognostic diagnosis based on three-dimensional magnetic resonance imaging (MRI) are essential for the proper treatment of gliomas. We developed a rapid, automated pipeline to segment gliomas and can accurately predict patient survival prognosis based on pretreatment MRI in this paper.T1-Gado MRI sequence images of gliomas are utilized to automatically segment gliomas using deep convolutional neural networks in this study. Nine machine learning models that combine radiomics features and clinical characteristics are leveraged to predict and compare the survival and prognosis of glioma patients. The results of the experiments show that all nine well-known learning model classification architectures can achieve accurate classification and reliable prediction results. The clinical decision curves show that, except for KNN and DT, all models perform well at various threshold probabilities.