Current State and Future Prospective of Radiomics in Glioma Imaging

Abstract

There has been enormous research interest in using radiomics or deep learning to predict genetic mutations within gliomas. These technologies were expected to solve the qualitative nature of radiographical images and allow us to analyze them quantitatively. Furthermore, these technologies were promised to make a direct connection between the biological characteristics of the tumor and radiographical information. On the other hand, it has now become clear that radiomics and deep learning have particular problems, such as over-fitting and domain shift, which must be solved to render them clinically applicable. The ideal diagnostic accuracy is still debatable, and it is still being determined whether radiomics or deep learning can achieve a correct diagnosis, even when non-tumor lesions are included. This review paper discusses the current state, issues, and future perspective of radiomics and deep learning in the glioma imaging research area.