Glioma Survival Analysis Empowered With Data Engineering—A Survey

Abstract

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.

m6A Regulatory Gene-Mediated Methylation Modification in Glioma Survival Prediction

“…Gliomas are the most common and malignant brain tumors. Despite the progress made in the diagnosis and treatment of brain tumors, the overall survival rate for glioma is quite low ( Wijethilake et al, 2021 ). Less than 10% of patients responds to standard therapy and lives longer than 2 years ( Olgun et al, 2021 ).…”

Overall Survival Prediction for Gliomas Using a Novel Compound Approach

“…Banerjee et al. designed two new radiological features, extracted from the brain segmentation atlas and spatial habitats and proved their effectiveness ( 46 ). We consider introducing these two features in future work to further improve survival prediction performance.…”

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

“…Early medical imaging research used basic statistical analysis to investigate associations with tumor prognostic factors. Laterally, interest has moved towards using machine learning and deep learning algorithms for tumor segmentation and prognosis analysis ( 17 , 18 ). This motivated us to look at the imaging and analysis techniques used to evaluate extra-axial tumors and how this work has evolved over time to incorporate methodological advancements.…”