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Predicting Survival in Patients with Brain Tumors

2022-09-01Recensão Acesso aberto
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dc.contributor.authorDi Noia, Christian
dc.contributor.authorGrist, James T.
dc.contributor.authorRiemer, Frank
dc.contributor.authorLyasheva, Maria
dc.contributor.authorFabozzi, Miriana
dc.contributor.authorCastelli, Mauro
dc.contributor.authorLodi, Raffaele
dc.contributor.authorTonon, Caterina
dc.contributor.authorRundo, Leonardo
dc.contributor.authorZaccagna, Fulvio
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2022-09-05T22:35:04Z
dc.date.available2022-09-05T22:35:04Z
dc.date.issued2022-09-01
dc.descriptionDi Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125
dc.description.abstractGiven 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent413117
dc.identifier.doi10.3390/diagnostics12092125
dc.identifier.issn2075-4418
dc.identifier.otherPURE: 46373385
dc.identifier.otherPURE UUID: e997b624-67d2-4611-98af-ff44c7680418
dc.identifier.othercrossref: 10.3390/diagnostics12092125
dc.identifier.otherScopus: 85138622821
dc.identifier.otherWOS: 000857672200001
dc.identifier.urihttp://hdl.handle.net/10362/143516
dc.identifier.urlhttps://www.scopus.com/pages/publications/85138622821
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000857672200001
dc.identifier.urlhttps://www.mdpi.com/2075-4418/12/9/2125
dc.language.isoeng
dc.peerreviewedyes
dc.subjectBrain tumors
dc.subjectArtificial intelligence
dc.subjectmachine learning
dc.subjectSurvival prediction
dc.subjectMagnetic Resonance Imaging
dc.subjectClinical Biochemistry
dc.subjectSDG 3 - Good Health and Well-being
dc.titlePredicting Survival in Patients with Brain Tumorsen
dc.title.subtitleCurrent State-of-the-Art of AI Methods Applied to MRIen
dc.typereview
degois.publication.firstPage1
degois.publication.issue9
degois.publication.lastPage16
degois.publication.titleDiagnostics
degois.publication.volume12
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)