SynthesisNeurosurgical review2025
Machine learning-based models and radiomics: can they be reliable predictors for meningioma recurrence? A systematic review and meta-analysis.
Synthesis in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of machine learning algorithms to detect digital addiction: a meta-analysis.Frontiers in psychiatry · 2026Pooled it
- MRI Reflects Meningioma Biology and Molecular Risk.Cancers · 2025Article
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
backgroundPredicting recurrence in meningioma patients is vital for improving long-term outcomes and tailoring personalized treatment strategies. While traditional diagnostic methods have advanced, accurately forecasting recurrence remains a persistent and critical challenge. This study explores the cutting-edge application of artificial intelligence (AI)-based models, which seamlessly integrate clinical, radiological, and pathological data, offering a transformative approach to enhancing the reliability and precision of recurrence prediction.
methodsEligible studies were identified through a comprehensive search of the Web of Science, Scopus, PubMed, and Embase databases. Extracted and synthesized metrics for analysis included accuracy, sensitivity, specificity, precision, F1 score, and area under the curve (AUC). Out of 2,971 studies screened, six met the inclusion criteria for systematic review, and three were included in the meta-analysis.
resultsThe pooled sensitivity and specificity of AI models were 0.86 [95% CI: 0.78-0.92] and 0.86 [95% CI: 0.81-0.90], respectively. The positive diagnostic likelihood ratio (DLR) was 6.33 [95% CI: 4.42-9.08], and the negative DLR was 0.16 [95% CI: 0.09-0.27]. The diagnostic odds ratio (DOR) was estimated at 40.11 [95% CI: 19.30-83.37], with a diagnostic score of 3.69 [95% CI: 2.96-4.42] and a pooled area under the curve (AUC) of 0.93 [95% CI: 0.90-0.95]. Subgroup analysis showed comparable sensitivity (RF: 0.88; LR: 0.84) and specificity (RF: 0.84; LR: 0.84) with no significant heterogeneity (I² = 0%).
conclusionsThese findings highlight the potential of AI-based models to predict meningioma recurrence, offer superior diagnostic accuracy, and aid clinical decision-making. Integrating clinical, radiological, and pathological data through AI-driven models demonstrates substantial promise in enhancing the reliability and efficiency of recurrence forecasting.
Indexed as
Identifiers
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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.