A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction

Abstract

Abstract Background Meningiomas constitute the most prevalent primary intracranial tumors, accounting for approximately 39% of all central nervous system tumors and representing a substantial neurosurgical challenge. Objective This review aims to examine and summarize the current applications of artificial intelligence (AI) technologies throughout the diagnosis and treatment processes of meningiomas. Methods A 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. Results AI 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. Conclusion AI technology represents a formidable new instrument for the precise diagnosis and treatment of meningiomas, showing notable potential for clinical translation.

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