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Article Abstract
Introduction: Gliomas are the most common primary malignant intraparenchymal brain tumors with a dismal prognosis. With growing advances in artificial intelligence, machine learning and deep learning models are being utilized for preoperative, intraoperative and postoperative neurological decision-making. We aimed to compile published literature in one format and evaluate the quality of level 1a evidence currently available.
Methodology: Using PRISMA guidelines, a comprehensive literature search was conducted within databases including Medline, Scopus, and Cochrane Library, and records with the application of artificial intelligence in glioma management were included. The AMSTAR 2 tool was used to assess the quality of systematic reviews and meta-analyses by two independent researchers.
Results: From 812 studies, 23 studies were included. AMSTAR II appraised most reviews as either low or critically low in quality. Most reviews failed to deliver in critical domains related to the exclusion of studies, appropriateness of meta-analytical methods, and assessment of publication bias. Similarly, compliance was lowest in non-critical areas related to study design selection and the disclosure of funding sources in individual records. Evidence is moderate to low in quality in reviews on multiple neuro-oncological applications, low quality in glioma diagnosis and individual molecular markers like MGMT promoter methylation status, IDH, and 1p19q identification, and critically low in tumor segmentation, glioma grading, and multiple molecular markers identification.
Conclusion: AMSTAR 2 is a robust tool to identify high-quality systematic reviews. There is a paucity of high-quality systematic reviews on the utility of artificial intelligence in glioma management, with some demonstrating critically low quality. Therefore, caution must be exercised when drawing inferences from these results.
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| http://dx.doi.org/10.1016/j.jocn.2024.110926 | DOI Listing |
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