Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Abstract

Background: Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its diverse manifestations. Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and efficiency of meningioma segmentation from MRI scans. This systematic review and meta-analysis assess the effectiveness of CNN models in segmenting meningioma using MRI.

Methods: Following the PRISMA guidelines, we searched PubMed, Embase, and Web of Science from their inception to December 20, 2023, to identify studies that used CNN models for meningioma segmentation in MRI. Methodological quality of the included studies was assessed using the CLAIM and QUADAS-2 tools. The primary variable was segmentation accuracy, which was evaluated using the Sørensen-Dice coefficient. Meta-analysis, subgroup analysis, and meta-regression were performed to investigate the effects of MRI sequence, CNN architecture, and training dataset size on model performance.

Results: Nine studies, comprising 4,828 patients, were included in the analysis. The pooled Dice score across all studies was 89% (95% CI: 87-90%). Internal validation studies yielded a pooled Dice score of 88% (95% CI: 85-91%), while external validation studies reported a pooled Dice score of 89% (95% CI: 88-90%). Models trained on multiple MRI sequences consistently outperformed those trained on single sequences. Meta-regression indicated that training dataset size did not significantly influence segmentation accuracy.

Conclusion: CNN models are highly effective for meningioma segmentation in MRI, particularly during the use of diverse datasets from multiple MRI sequences. This finding highlights the importance of data quality and imaging sequence selection in the development of CNN models. Standardization of MRI data acquisition and preprocessing may improve the performance of CNN models, thereby facilitating their clinical adoption for the optimal diagnosis and treatment of meningioma.