SynthesisNeuroradiology2021
Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.
Synthesis in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 8 of them syntheses that pooled it.
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
45 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment.Frontiers in neurology · 2024Pooled it
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment.European radiology · 2022Pooled it
- Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Reproducibility of radiomics quality score: an intra- and inter-rater reliability study.European radiology · 2024Trial
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Non-invasive Prediction of Meningioma Tumor Grade by Quantification of Shape-based Radiomics Features and Surface Regularity.Clinical neuroradiology · 2026Article
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- Metastatic Meningioma: Neuroradiologic and Molecular Imaging Perspectives.Radiology. Imaging cancer · 2025Review
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- The intelligent imaging revolution: artificial intelligence in MRI and MRS acquisition and reconstruction.Magma (New York, N.Y.) · 2024Article
- Review
- The Evolving Classification of Meningiomas: Integration of Molecular Discoveries to Inform Patient Care.Cancers · 2024Review
- Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.BMC medical imaging · 2024Article
- Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.Frontiers in computational neuroscience · 2024Article
- Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.Biomedicines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI.
methodsArticles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool.
resultsIn all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, -8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54-0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84-0.93) with a standard error of 0.02.
conclusionsMachine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.
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.