ReviewDiscover oncology2026
A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.
Review in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Diagnostic imaging and emerging image-guided neurointervention in brain tumors: a narrative review.Frontiers in radiology · 2026Review
- Multimodal brain MRI and clinical data in olfactory groove meningioma: a prospective data report.Frontiers in radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundMeningiomas constitute the most prevalent primary intracranial tumors, accounting for approximately 39% of all central nervous system tumors and representing a substantial neurosurgical challenge.
objectiveThis review aims to examine and summarize the current applications of artificial intelligence (AI) technologies throughout the diagnosis and treatment processes of meningiomas.
methodsA search was conducted in the Web of Science core collection and Scopus and PubMed, databases on November 9, 2025, utilizing a search strategy that incorporated the term “meningioma” along with related AI terminologies in the title. Literature was screened based on pre-defined inclusion and exclusion criteria, resulting in 52 articles being selected for this review.
resultsAI technologies have demonstrated considerable promise and added value in the management of meningiomas. In image analysis, deep learning models have facilitated automatic and highly precise tumor segmentation, significantly outperforming traditional manual methods. Regarding pathological prediction, AI models have successfully non-invasively predicted crucial biomarkers, such as WHO classification and the Ki-67 index, from preoperative MRI scans. In prognostic prediction, AI models have exhibited robust capabilities in forecasting overall survival, progression-free survival, and recurrence risk.
conclusionAI technology represents a formidable new instrument for the precise diagnosis and treatment of meningiomas, showing notable potential for clinical translation.
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.