OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review Article Swipe
Waverly Rose Brim
,
Leon Jekel
,
Gabriel Cassinelli Petersen
,
Harry Subramanian
,
Tal Zeevi
,
Seyedmehdi Payabvash
,
Khaled Bousabarah
,
MingDe Lin
,
Jin Cui
,
Alexandria Brackett
,
Ajay Mahajan
,
Michele H. Johnson
,
Amit Mahajan
,
Mariam Aboian
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/noajnl/vdab071.067
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/noajnl/vdab071.067
Purpose Medical staging, surgical planning, and therapeutic decisions are significantly different for brain metastases versus gliomas. Machine learning (ML) algorithms have been developed to differentiate these pathologies. We performed a systematic review to characterize ML methods and to evaluate their accuracy. Methods Studies on the application of machine learning in neuro-oncology were searched in Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection. A search strategy was designed in compliance with a clinical librarian and confirmed by a second librarian. The search strategy comprised of controlled vocabulary including artificial intelligence, machine learning, deep learning, magnetic resonance imaging, and glioma. The initial search was performed in October 2020 and then updated in February 2021. Candidate articles were screened in Covidence by at least two reviewers each. A bias analysis was conducted in agreement with TRIPOD, a bias assessment tool similar to CLAIM. Results Twenty-nine articles were used for data extraction. Four articles specified model development for solitary brain metastases. Classical ML (cML) algorithms represented 85% of models used, while deep learning (DL) accounted for 15%. cML algorithms performed with an average accuracy, sensitivity, and specificity of 82%, 78%, 88%, respectively; DL performed 84%, 79%, 81%. The support vector machine (SVM) algorithm was the most common used cML model in the literature and convolutional neural networks (CNN) were standard for DL models. We also found T1, T1 post-gadolinium and T2 sequences were most commonly used for feature extraction. Preliminary TRIPOD analysis yielded an average score of 14.25 (range 8–18). Conclusion ML algorithms that can accurately classify glioma from brain metastases have been developed. SVM and CNN are leading approaches with high accuracy. Standardized algorithm performance reporting is a clear limitation to be addressed in future studies.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/noajnl/vdab071.067
- https://academic.oup.com/noa/article-pdf/3/Supplement_3/iii17/39606772/vdab071.067.pdf
- OA Status
- gold
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3191664381
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3191664381Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/noajnl/vdab071.067Digital Object Identifier
- Title
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OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic reviewWork title
- Type
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reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Waverly Rose Brim, Leon Jekel, Gabriel Cassinelli Petersen, Harry Subramanian, Tal Zeevi, Seyedmehdi Payabvash, Khaled Bousabarah, MingDe Lin, Jin Cui, Alexandria Brackett, Ajay Mahajan, Michele H. Johnson, Amit Mahajan, Mariam AboianList of authors in order
- Landing page
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https://doi.org/10.1093/noajnl/vdab071.067Publisher landing page
- PDF URL
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https://academic.oup.com/noa/article-pdf/3/Supplement_3/iii17/39606772/vdab071.067.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://academic.oup.com/noa/article-pdf/3/Supplement_3/iii17/39606772/vdab071.067.pdfDirect OA link when available
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Artificial intelligence, Machine learning, Algorithm, Computer science, Support vector machine, MEDLINE, Convolutional neural network, Deep learning, Systematic review, Medicine, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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