Predicting survival in malignant glioma using artificial intelligence
Abstract Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | European Journal of Medical Research |
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| Online Access: | https://doi.org/10.1186/s40001-025-02339-3 |
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| author | Wireko Andrew Awuah Adam Ben-Jaafar Subham Roy Princess Afia Nkrumah-Boateng Joecelyn Kirani Tan Toufik Abdul-Rahman Oday Atallah |
| author_facet | Wireko Andrew Awuah Adam Ben-Jaafar Subham Roy Princess Afia Nkrumah-Boateng Joecelyn Kirani Tan Toufik Abdul-Rahman Oday Atallah |
| author_sort | Wireko Andrew Awuah |
| collection | DOAJ |
| description | Abstract Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan–Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. This study highlights the comparative effectiveness of imaging-based, non-imaging and combined AI models. Imaging models excel at identifying tumour-specific features through radiomics, achieving high predictive accuracy. Non-imaging approaches also excel in utilising clinical and genetic data to provide complementary insights, whilst combined methods integrate multiple data modalities and have the greatest potential for accurate survival prediction. Limitations include data heterogeneity, interpretability challenges and computational demands, particularly in resource-limited settings. Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. Ultimately, the integration of advanced AI techniques promises to transform glioma management by enabling personalised treatment strategies and improved prognostic accuracy. |
| format | Article |
| id | doaj-art-ee96110577ba434399305478bc6d07c6 |
| institution | Kabale University |
| issn | 2047-783X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj-art-ee96110577ba434399305478bc6d07c62025-02-02T12:13:54ZengBMCEuropean Journal of Medical Research2047-783X2025-01-0130111110.1186/s40001-025-02339-3Predicting survival in malignant glioma using artificial intelligenceWireko Andrew Awuah0Adam Ben-Jaafar1Subham Roy2Princess Afia Nkrumah-Boateng3Joecelyn Kirani Tan4Toufik Abdul-Rahman5Oday Atallah6Department of Research, Toufik’s World Medical AssociationSchool of Medicine, University College DublinHull York Medical School, University of YorkUniversity of Ghana Medical SchoolFaculty of Biology, Medicine and Health, University of ManchesterDepartment of Research, Toufik’s World Medical AssociationDepartment of Neurosurgery, Carl Von Ossietzky University OldenburgAbstract Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan–Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. This study highlights the comparative effectiveness of imaging-based, non-imaging and combined AI models. Imaging models excel at identifying tumour-specific features through radiomics, achieving high predictive accuracy. Non-imaging approaches also excel in utilising clinical and genetic data to provide complementary insights, whilst combined methods integrate multiple data modalities and have the greatest potential for accurate survival prediction. Limitations include data heterogeneity, interpretability challenges and computational demands, particularly in resource-limited settings. Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. Ultimately, the integration of advanced AI techniques promises to transform glioma management by enabling personalised treatment strategies and improved prognostic accuracy.https://doi.org/10.1186/s40001-025-02339-3Malignant gliomaArtificial intelligence (AI)Machine learning (ML)Deep learning (DL)Survival prediction approaches |
| spellingShingle | Wireko Andrew Awuah Adam Ben-Jaafar Subham Roy Princess Afia Nkrumah-Boateng Joecelyn Kirani Tan Toufik Abdul-Rahman Oday Atallah Predicting survival in malignant glioma using artificial intelligence European Journal of Medical Research Malignant glioma Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Survival prediction approaches |
| title | Predicting survival in malignant glioma using artificial intelligence |
| title_full | Predicting survival in malignant glioma using artificial intelligence |
| title_fullStr | Predicting survival in malignant glioma using artificial intelligence |
| title_full_unstemmed | Predicting survival in malignant glioma using artificial intelligence |
| title_short | Predicting survival in malignant glioma using artificial intelligence |
| title_sort | predicting survival in malignant glioma using artificial intelligence |
| topic | Malignant glioma Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Survival prediction approaches |
| url | https://doi.org/10.1186/s40001-025-02339-3 |
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