MRI and Meningioma Research: Hotspots and Trends via Bibliometric Analysis

Abstract

BACKGROUND: Precision meningioma diagnosis requires MRI advancements, yet faces three barriers: (1) limited clinical translation, (2) inconsistent multimodal data standards, and (3) mismatched algorithm-resource allocation. A bibliometric analysis can guide evidence-based innovation. METHODS: We conducted a comprehensive bibliometric analysis of 4,280 Web of Science articles using CiteSpace, Bibliometrix, and SciExplorer, with dual screening to ensure data quality. RESULTS: Meningioma MRI research exhibited an S-shaped growth pattern. Research hotspots are transitioning toward AI applications. 502 core authors contributed to 83% of publications, with notable cross-disciplinary collaboration. The U.S. and China dominated production, while Europe demonstrated exceptional efficiency. Institutions, including Harvard, led development. Seventeen core journals conformed to Bradford's law, with the knowledge foundation established by highly cited papers in the field. DISCUSSION: The findings reveal an AI-guideline temporal gap and a field-strength validation deficit, underscoring the need for equitable, low-field-compatible AI tools. CONCLUSION: We systematically delineate three evolutionary stages: structural imaging, functional integration, and intelligent analysis. AI-driven models have achieved enhanced diagnostic accuracy (AUC 0.82-0.97), but remain limited by heterogeneous data standards, low algorithm interpretability, and uneven global resources. Multicentre standardized protocols, interpretable AI frameworks, and lightweight algorithms compatible with ≤1.5 T scanners should be prioritised. By integrating burst-sigma mapping with global equipment metrics, we provide quantitative evidence supporting a field-strength-agnostic strategy for equitable AI deployment.

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