ArticleNeuro-oncology advances
Assessment of the emerging role of AI in diagnosing gliomas using MRI: Systematic review and meta-analysis.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Brain Cancer: Molecular Alterations and Emerging Trends in Neuropharmacology.International journal of molecular sciences · 2026Review
- Heterogeneity, Measurement, and Clinical Implications of Oxygenation, Cell Signaling, and Redox Biology in Glioblastoma and Adult Diffuse Gliomas, with Context from Other Brain Tumors.Antioxidants (Basel, Switzerland) · 2026Review
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the emerging role of artificial intelligence (AI) in glioma grading, its clinical adoption remains in its early stages. This meta-analysis aims to assess the role of AI in differentiating glioma grades using magnetic resonance imaging (MRI). Twenty-five studies matched the inclusion criteria and were included after systematic searches through "PubMed" electronic database. The quality of the included studies was assessed utilizing Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A bivariate random-effects model was employed to estimate the pooled effect of the sensitivity and specificity, followed by an estimation of the summary receiver operating characteristic (SROC) curve. The overall results suggest relatively high sensitivity and specificity among the assessed AI methods for discriminating glioma grades. Convolutional Neural Networks (CNN) demonstrated the highest diagnostic accuracy, with a sensitivity of 93% (95% CI: 88%-97%) and specificity of 92% (95% CI: 90%-94%). This meta-analysis highlights the potential role of AI models based on MRI in supporting clinicians in glioma grading.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.