Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies Article Swipe
Thomas C. Booth
,
Mariusz Grzeda
,
Alysha Chelliah
,
Andrei Roman
,
Ayisha Al Busaidi
,
Carmen Dragos
,
Haris Shuaib
,
Aysha Luis
,
Ayesha Mirchandani
,
Burcu Alparslan
,
Nina Mansoor
,
José Pedro Lavrador
,
Francesco Vergani
,
Keyoumars Ashkan
,
Marc Modat
,
Sébastien Ourselin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2022.799662
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2022.799662
Objective Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies. Methods Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018–01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965). Results Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC. Conclusion ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2022.799662
- OA Status
- gold
- Cited By
- 33
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210586081
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210586081Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fonc.2022.799662Digital Object Identifier
- Title
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Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning StudiesWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-31Full publication date if available
- Authors
-
Thomas C. Booth, Mariusz Grzeda, Alysha Chelliah, Andrei Roman, Ayisha Al Busaidi, Carmen Dragos, Haris Shuaib, Aysha Luis, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, José Pedro Lavrador, Francesco Vergani, Keyoumars Ashkan, Marc Modat, Sébastien OurselinList of authors in order
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https://doi.org/10.3389/fonc.2022.799662Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fonc.2022.799662Direct OA link when available
- Concepts
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Glioblastoma, Meta-analysis, Medicine, Bioinformatics, Oncology, Medical physics, Cancer research, Internal medicine, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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33Total citation count in OpenAlex
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2025: 7, 2024: 12, 2023: 9, 2022: 4Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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