Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI
Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases,...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/9/2125 |
| _version_ | 1797489492677885952 |
|---|---|
| author | Christian di Noia James T. Grist Frank Riemer Maria Lyasheva Miriana Fabozzi Mauro Castelli Raffaele Lodi Caterina Tonon Leonardo Rundo Fulvio Zaccagna |
| author_facet | Christian di Noia James T. Grist Frank Riemer Maria Lyasheva Miriana Fabozzi Mauro Castelli Raffaele Lodi Caterina Tonon Leonardo Rundo Fulvio Zaccagna |
| author_sort | Christian di Noia |
| collection | DOAJ |
| description | Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques. |
| first_indexed | 2024-03-10T00:17:23Z |
| format | Article |
| id | doaj.art-d60b623be05d4c6d94fdde5fcf75f059 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T00:17:23Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-d60b623be05d4c6d94fdde5fcf75f0592023-11-23T15:49:08ZengMDPI AGDiagnostics2075-44182022-09-01129212510.3390/diagnostics12092125Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRIChristian di Noia0James T. Grist1Frank Riemer2Maria Lyasheva3Miriana Fabozzi4Mauro Castelli5Raffaele Lodi6Caterina Tonon7Leonardo Rundo8Fulvio Zaccagna9Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, ItalyDepartment of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UKMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, N-5021 Bergen, NorwayDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UKCentro Medico Polispecialistico (CMO), 80058 Torre Annunziata, ItalyNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalDepartment of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, ItalyDepartment of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, ItalyDepartment of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, ItalyDepartment of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, ItalyGiven growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.https://www.mdpi.com/2075-4418/12/9/2125brain tumorsartificial intelligencemachine learningsurvival predictionmagnetic resonance imaging |
| spellingShingle | Christian di Noia James T. Grist Frank Riemer Maria Lyasheva Miriana Fabozzi Mauro Castelli Raffaele Lodi Caterina Tonon Leonardo Rundo Fulvio Zaccagna Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI Diagnostics brain tumors artificial intelligence machine learning survival prediction magnetic resonance imaging |
| title | Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI |
| title_full | Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI |
| title_fullStr | Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI |
| title_full_unstemmed | Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI |
| title_short | Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI |
| title_sort | predicting survival in patients with brain tumors current state of the art of ai methods applied to mri |
| topic | brain tumors artificial intelligence machine learning survival prediction magnetic resonance imaging |
| url | https://www.mdpi.com/2075-4418/12/9/2125 |
| work_keys_str_mv | AT christiandinoia predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT jamestgrist predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT frankriemer predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT marialyasheva predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT mirianafabozzi predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT maurocastelli predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT raffaelelodi predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT caterinatonon predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT leonardorundo predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri AT fulviozaccagna predictingsurvivalinpatientswithbraintumorscurrentstateoftheartofaimethodsappliedtomri |