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

Fangliang Huang; Vladimir Y. Mariano

doi:10.37965/jait.2025.0681

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

Authors

Fangliang Huang College of Computing and Information Technologies, National University, Manila, Philippines & School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China https://orcid.org/0009-0007-5665-8222
Vladimir Y. Mariano College of Computing and Information Technologies, National University, Manila, Philippines

DOI:

https://doi.org/10.37965/jait.2025.0681

Keywords:

glioma, segmentation, survival prognosis, deep learning

Abstract

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.