Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis
Makaleler > Derleme · 2025
Yazarlar: Parsia Noori Mirtaheri, Matin Akhbari, Farnaz Najafi, Hoda Mehrabi, Ali Babapour, Zahra Rahimian, Amirhossein Rigi, Saeid Rahbarbaghbani, Hesam Mobaraki, Sanaz Masoumi, Danial Nouri, Seyedeh-Tarlan Mirzohreh, Seyyed Kiarash Sadat Rafiei, Mahsa Asadi Anar, Zahra Golkar, Yasaman Asadollah Salmanpour, Ali Vesali Mahmoud, Mohammad Sadra Gholami Chahkand, Maryam Khodaei
Frontiers in Neurology, Uluslararası, Wos, 2025, cilt 16, ss 1536751-1536751
DOI: 10.3389/fneur.2025.1536751
Özet
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 (I2 = 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