ReviewCancers2022
Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of radiological prediction of ki 67 proliferation index of meningioma.Neurosurgical review · 2024Pooled it
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- 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
- Multisequence magnetic resonance imaging habitat analysis for pre-operative meningioma grade prediction.Quantitative imaging in medicine and surgery · 2025Article
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features.Journal of neuro-oncology · 2025Article
- Article
- Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.Cancers · 2023Article
- Review
- Meningiomas with CNS invasion.Frontiers in neuroscience · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.
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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.