A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.

中文翻译：

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人工智能在脑膜瘤分级和分类中的研究进展

脑膜瘤是一种常见的原发性中枢神经系统肿瘤。脑膜瘤的组织学特征根据级别和亚型的不同而显着不同，导致治疗和预后的差异。因此，脑膜瘤的早期诊断、分级和分型对于制定全面、个体化的诊疗方案至关重要。人工智能（AI）在医学影像领域的进步，特别是放射组学和深度学习（DL），促进了脑膜瘤分级和分类研究的不断增加。这些技术快速准确、完全自动化学习、无创客观，能够高效、无创地预测脑膜瘤的分级和分类，为临床治疗和预后提供有价值的帮助。本文对放射组学和深度学习在脑膜瘤分级和分类方面的研究进展进行总结和分析。还重点介绍了现有的研究成果、局限性以及未来改进的建议，旨在促进人工智能未来在脑膜瘤诊断和治疗中的应用。