Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis

Abstract

Background: Accurate preoperative grading of meningiomas is crucial for selecting the most suitable treatment strategies and predicting patient outcomes. Traditional MRI-based assessments are often insufficient to distinguish between low- and high-grade meningiomas reliably. Deep learning (DL) models have emerged as promising tools for automated histopathological grading using imaging data. This systematic review and meta-analysis aimed to comprehensively evaluate the diagnostic performance of deep learning (DL) models for meningioma grading. Methods: This study was conducted in accordance with the PRISMA-DTA guidelines and was prospectively registered on the Open Science Framework. A systematic search of PubMed, Scopus, and Web of Science was performed up to March 2025. Studies using DL models to classify meningiomas based on imaging data were included. A random-effects meta-analysis was used to pool sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). A bivariate random-effects model was used to fit the summary receiver operating characteristic (SROC) curve. Study quality was assessed using the Newcastle-Ottawa Scale, and publication bias was evaluated using Egger's test. Results: Twenty-seven studies involving 13,130 patients were included. The pooled sensitivity was 92.31 (95 CI: 92.1-92.52), specificity 95.3 (95 CI: 95.11-95.48), and accuracy 97.97 (95 CI: 97.35-97.98), with an AUC of 0.97 (95 CI: 0.96-0.98). The bivariate SROC curve demonstrated excellent diagnostic performance, characterized by a relatively narrow 95 confidence interval despite moderate to high heterogeneity (I-2 = 79.7, p < 0.001). Conclusion: DL models demonstrate high diagnostic accuracy for automatic meningioma grading and could serve as valuable clinical decision-support tools. Systematic review registration: DOI: 10.17605/OSF.IO/RXEBM