Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Journal of Clinical Neuroscience, 2021, 89, pp. 177-198
- Issue Date:
- 2021-07
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1-s2.0-S0967586821002241-main.pdf | 1.44 MB |
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Buchlak, QD | |
dc.contributor.author | Esmaili, N | |
dc.contributor.author | Leveque, J-C | |
dc.contributor.author | Bennett, C | |
dc.contributor.author | Farrokhi, F | |
dc.contributor.author |
Piccardi, M |
|
dc.date.accessioned | 2022-03-31T04:21:11Z | |
dc.date.available | 2021-04-30 | |
dc.date.available | 2022-03-31T04:21:11Z | |
dc.date.issued | 2021-07 | |
dc.identifier.citation | Journal of Clinical Neuroscience, 2021, 89, pp. 177-198 | |
dc.identifier.issn | 0967-5868 | |
dc.identifier.issn | 1532-2653 | |
dc.identifier.uri | http://hdl.handle.net/10453/155775 | |
dc.description.abstract | Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartof | Journal of Clinical Neuroscience | |
dc.relation.isbasedon | 10.1016/j.jocn.2021.04.043 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | 1103 Clinical Sciences, 1109 Neurosciences | |
dc.subject.classification | Neurology & Neurosurgery | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Artificial Intelligence | |
dc.subject.mesh | Brain Neoplasms | |
dc.subject.mesh | Glioma | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Neuroimaging | |
dc.subject.mesh | Neurosurgical Procedures | |
dc.subject.mesh | Support Vector Machine | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Artificial Intelligence | |
dc.subject.mesh | Brain Neoplasms | |
dc.subject.mesh | Glioma | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Neuroimaging | |
dc.subject.mesh | Neurosurgical Procedures | |
dc.subject.mesh | Support Vector Machine | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Glioma | |
dc.subject.mesh | Brain Neoplasms | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Neurosurgical Procedures | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Artificial Intelligence | |
dc.subject.mesh | Neuroimaging | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Support Vector Machine | |
dc.subject.mesh | Neural Networks, Computer | |
dc.title | Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. | |
dc.type | Journal Article | |
utslib.citation.volume | 89 | |
utslib.location.activity | Scotland | |
utslib.for | 1103 Clinical Sciences | |
utslib.for | 1109 Neurosciences | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering | |
utslib.copyright.status | closed_access | * |
pubs.consider-herdc | true | |
dc.date.updated | 2022-03-31T04:21:09Z | |
pubs.publication-status | Published | |
pubs.volume | 89 |
Abstract:
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
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