SynthesisNeuroradiology2024
Meningioma grading via diagnostic imaging: A systematic review and meta-analysis.
Synthesis in Neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Preoperative prediction of meningioma grade based on synthetic MRI quantitative T1 and T2 mapping.Japanese journal of radiology · 2026Article
- Synthetic MRI and Delta Histogram Analysis: A Step Forward in Noninvasive Meningioma Grading.Radiology. Imaging cancer · 2026Article
- Somatostatin receptor PET in meningioma: diagnosis, therapy, and surveillance.Nuclear medicine communications · 2026Review
- Diagnostic Value of Routine MRI Parameters in Identifying High-Grade Intracranial Meningiomas.Neuroradiology · 2026Article
- Peritumoral Cystic Meningioma With Diagnostic Challenges: Two Case Reports and a Literature Review.Cureus · 2026Article
- Diagnostic imaging and emerging image-guided neurointervention in brain tumors: a narrative review.Frontiers in radiology · 2026Review
- ADC values compared to tumor grade and Ki-67 proliferation index detected by a digital image analysis program in meningiomas.Acta radiologica (Stockholm, Sweden : 1987) · 2025Article
- Article
- Identification of key elements in MRI reporting of intracranial meningiomas based on a nationwide survey of clinical experts in Germany.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeMeningioma is the most common intracranial tumor, graded on pathology using WHO criteria to predict tumor course and treatment. However, pathological grading via biopsy may not be possible in cases with poor surgical access due to tumor location. Therefore, our systematic review aims to evaluate whether diagnostic imaging features can differentiate high grade (HG) from low grade (LG) meningiomas as an alternative to pathological grading.
methodsThree databases were searched for primary studies that either use routine magnetic resonance imaging (MRI) or computed tomography (CT) to assess pathologically WHO-graded meningiomas. Two investigators independently screened and extracted data from included studies.
results24 studies met our inclusion criteria with 12 significant (p < 0.05) CT and MRI features identified for differentiating HG from LG meningiomas. Cystic changes in the tumor had the highest specificity (93.4%) and irregular tumor-brain interface had the highest positive predictive value (65.0%). Mass effect had the highest sensitivity (81.0%) and negative predictive value (90.7%) of all imaging features. Imaging feature with the highest accuracy for identifying HG disease was irregular tumor-brain interface (79.7%). Irregular tumor-brain interface and heterogenous tumor enhancement had the highest AUC values of 0.788 and 0.703, respectively.
conclusionOur systematic review highlight imaging features that can help differentiate HG from LG meningiomas.
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