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dc.contributor.authorBooth, Thomas C
dc.contributor.authorGrzeda, Mariusz
dc.contributor.authorChelliah, Alysha
dc.contributor.authorRoman, Andrei
dc.contributor.authorAl Busaidi, Ayisha
dc.contributor.authorDragos, Carmen
dc.contributor.authorShuaib, Haris
dc.contributor.authorLuis, Aysha
dc.contributor.authorMirchandani, Ayesha
dc.contributor.authorAlparslan, Burcu
dc.contributor.authorMansoor, Nina
dc.contributor.authorLavrador, Jose
dc.contributor.authorVergani, Francesco
dc.contributor.authorAshkan, Keyoumars
dc.contributor.authorModat, Marc
dc.contributor.authorOurselin, Sebastien
dc.date.accessioned2022-02-14T16:00:39Z
dc.date.available2022-02-14T16:00:39Z
dc.date.issued2022
dc.date.submitted2021-10-21
dc.identifier.issn2234-943X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334008
dc.description.abstractOBJECTIVE: 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.
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectOncology
dc.subjectglioblastoma
dc.subjectmachine learning
dc.subjectmonitoring biomarkers
dc.subjectmeta-analysis
dc.subjectartificial intelligence
dc.subjecttreatment response
dc.subjectdeep learning
dc.subjectglioma
dc.titleImaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.
dc.typeArticle
dc.date.updated2022-02-14T16:00:38Z
prism.publicationNameFront Oncol
prism.volume12
dc.identifier.doi10.17863/CAM.81420
dcterms.dateAccepted2022-01-03
rioxxterms.versionofrecord10.3389/fonc.2022.799662
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2234-943X
cam.issuedOnline2022-01-31


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