Glioma Survival Analysis Empowered With Data Engineering—A Survey
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 propose...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9378513/ |
| _version_ | 1818933956578377728 |
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| author | Navodini Wijethilake Dulani Meedeniya Charith Chitraranjan Indika Perera Mobarakol Islam Hongliang Ren |
| author_facet | Navodini Wijethilake Dulani Meedeniya Charith Chitraranjan Indika Perera Mobarakol Islam Hongliang Ren |
| author_sort | Navodini Wijethilake |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T04:56:37Z |
| format | Article |
| id | doaj.art-b1c7097614404317a7350f42f6ce2d7c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:56:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1c7097614404317a7350f42f6ce2d7c2022-12-21T19:52:42ZengIEEEIEEE Access2169-35362021-01-019431684319110.1109/ACCESS.2021.30659659378513Glioma Survival Analysis Empowered With Data Engineering—A SurveyNavodini Wijethilake0https://orcid.org/0000-0001-9620-8233Dulani Meedeniya1https://orcid.org/0000-0002-4520-3819Charith Chitraranjan2https://orcid.org/0000-0003-3205-2211Indika Perera3https://orcid.org/0000-0001-5660-248XMobarakol Islam4Hongliang Ren5https://orcid.org/0000-0002-6488-1551Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri LankaDepartment of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri LankaBiomedical Image Analysis Group, Imperial College London, London, U.KDepartment of Biomedical Engineering, National University of Singapore, SingaporeSurvival 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.https://ieeexplore.ieee.org/document/9378513/Survival predictionrisk analysisgliomagenomicsradiomicsradiogenomics |
| spellingShingle | Navodini Wijethilake Dulani Meedeniya Charith Chitraranjan Indika Perera Mobarakol Islam Hongliang Ren Glioma Survival Analysis Empowered With Data Engineering—A Survey IEEE Access Survival prediction risk analysis glioma genomics radiomics radiogenomics |
| title | Glioma Survival Analysis Empowered With Data Engineering—A Survey |
| title_full | Glioma Survival Analysis Empowered With Data Engineering—A Survey |
| title_fullStr | Glioma Survival Analysis Empowered With Data Engineering—A Survey |
| title_full_unstemmed | Glioma Survival Analysis Empowered With Data Engineering—A Survey |
| title_short | Glioma Survival Analysis Empowered With Data Engineering—A Survey |
| title_sort | glioma survival analysis empowered with data engineering x2014 a survey |
| topic | Survival prediction risk analysis glioma genomics radiomics radiogenomics |
| url | https://ieeexplore.ieee.org/document/9378513/ |
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