Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

[Display omitted] oMachine learning has been widely applied to MRI to detect and diagnose gliomas.oNon-invasive detection of genetics has the potential to supersede biopsy procedures.oMachine learning has demonstrated strong performance and clinical utility.oPerformance metrics from 153 articles wer...

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Vydáno v:Journal of clinical neuroscience Ročník 89; s. 177 - 198
Hlavní autoři: Buchlak, Quinlan D., Esmaili, Nazanin, Leveque, Jean-Christophe, Bennett, Christine, Farrokhi, Farrokh, Piccardi, Massimo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2021
Témata:
Brain tumor classification
Convolutional neural networks
Deep learning
FLAIR
Glioblastoma
Glioma
Glioma grading
Image processing
Machine learning
Multimodal neuroimaging
Neurosurgery
Radiomics
T1-MR image
T2-MR image
T1-MR image
Image processing
Multimodal neuroimaging
Glioblastoma
Glioma grading
Neurosurgery
Radiomics
Deep learning
Brain tumor classification
Glioma
T2-MR image
Machine learning
FLAIR
Convolutional neural networks
ISSN:0967-5868, 1532-2653, 1532-2653
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Abstract [Display omitted] oMachine learning has been widely applied to MRI to detect and diagnose gliomas.oNon-invasive detection of genetics has the potential to supersede biopsy procedures.oMachine learning has demonstrated strong performance and clinical utility.oPerformance metrics from 153 articles were synthesized to facilitate benchmarking.oDeep language models can facilitate systematic review article screening. 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.
AbstractList 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.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.
[Display omitted] oMachine learning has been widely applied to MRI to detect and diagnose gliomas.oNon-invasive detection of genetics has the potential to supersede biopsy procedures.oMachine learning has demonstrated strong performance and clinical utility.oPerformance metrics from 153 articles were synthesized to facilitate benchmarking.oDeep language models can facilitate systematic review article screening. 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.
Author Farrokhi, Farrokh
Piccardi, Massimo
Leveque, Jean-Christophe
Buchlak, Quinlan D.
Esmaili, Nazanin
Bennett, Christine
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ISSN 0967-5868
1532-2653
IngestDate Thu Oct 02 10:40:19 EDT 2025
Tue Nov 18 22:13:21 EST 2025
Sat Nov 29 07:09:01 EST 2025
Fri Feb 23 02:43:58 EST 2024
Tue Oct 14 19:30:45 EDT 2025
IsPeerReviewed true
IsScholarly true
Keywords T1-MR image
Image processing
Multimodal neuroimaging
Glioblastoma
Glioma grading
Neurosurgery
Radiomics
Deep learning
Brain tumor classification
Glioma
T2-MR image
Machine learning
FLAIR
Convolutional neural networks
Language English
LinkModel OpenURL
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crossref_citationtrail_10_1016_j_jocn_2021_04_043
crossref_primary_10_1016_j_jocn_2021_04_043
elsevier_sciencedirect_doi_10_1016_j_jocn_2021_04_043
elsevier_clinicalkey_doi_10_1016_j_jocn_2021_04_043
PublicationCentury 2000
PublicationDate July 2021
2021-07-00
20210701
PublicationDateYYYYMMDD 2021-07-01
PublicationDate_xml – month: 07
  year: 2021
  text: July 2021
PublicationDecade 2020
PublicationTitle Journal of clinical neuroscience
PublicationYear 2021
Publisher Elsevier Ltd
Publisher_xml – name: Elsevier Ltd
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Snippet [Display omitted] oMachine learning has been widely applied to MRI to detect and diagnose gliomas.oNon-invasive detection of genetics has the potential to...
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...
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SubjectTerms Brain tumor classification
Convolutional neural networks
Deep learning
FLAIR
Glioblastoma
Glioma
Glioma grading
Image processing
Machine learning
Multimodal neuroimaging
Neurosurgery
Radiomics
T1-MR image
T2-MR image
Title Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0967586821002241
https://dx.doi.org/10.1016/j.jocn.2021.04.043
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