Glioma Survival Analysis Empowered With Data Engineering—A Survey

IEEE Access

Mobarakol Islam¹  Hongliang Ren²  Indika Perera³  Navodini Wijethilake³  Dulani Meedeniya³  Charith Chitraranjan³ 

[1] Biomedical Image Analysis Group, Imperial College London, London, U.K;Department of Biomedical Engineering, National University of Singapore, Singapore;Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka;

关键词: Survival prediction;    risk analysis;    glioma;    genomics;    radiomics;    radiogenomics;   

DOI  :  10.1109/ACCESS.2021.3065965

来源: DOAJ

【 摘 要 】

Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and genetic information. The remarkable advancements in imaging and high throughput omics and sequencing technologies have enabled the acquisition of this information of glioma patients efficiently, providing novel insights for survival estimation in the present day. Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival analysis approaches. In this survey paper, we explore the prognostic parameters acquired, utilizing diagnostic imaging techniques and genomic platforms for survival or risk estimation of glioma patients. Further, we review the techniques, learning and statistical analysis algorithms, along with their benefits and limitations used for prognosis prediction. Consequently, we highlight the challenges of the existing state-of-the-art survival prediction studies and propose future directions in the field of research.

【 授权许可】

Unknown