Overall survival time prediction for high grade gliomas based on sparse representation framework

Abstract

Accurate prognosis for high grade glioma (HGG) is of great clinical value since it would provide optimized guidelines for treatment planning. Previous imaging-based survival prediction generally relies on some features guided by clinical experiences, which limits the full utilization of biomedical image. In this paper, we propose a sparse representation-based radiomics framework to predict overall survival (OS) time of HGG. Firstly, we develop a patch-based sparse representation method to extract the high-throughput tumor texture features. Then, we propose to combine locality preserving projection and sparse representation to select discriminating features. Finally, we treat the OS time prediction as a classification task and apply sparse representation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.