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脑膜瘤的进展/复发:基于磁共振成像的影像学评价。

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging.

机构信息

Department of Radiology, Lanzhou University Second Hospita, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.

Department of Radiology, Lanzhou University Second Hospita, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.

出版信息

World Neurosurg. 2024 Jun;186:98-107. doi: 10.1016/j.wneu.2024.03.051. Epub 2024 Mar 17.

Abstract

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

摘要

脑膜瘤是最常见的原发性中枢神经系统肿瘤。首选治疗方法是最大限度地安全切除,而脑膜瘤的异质性导致预后不同。进展/复发(P/R)可发生在任何级别的脑膜瘤中,是手术治疗后的常见不良结局,也是术后再住院、二次手术和死亡的主要原因。P/R 的早期预测在术后管理、进一步辅助治疗和患者随访中起着重要作用。因此,彻底分析脑膜瘤的异质性并借助无创术前影像学预测术后 P/R 至关重要。近年来,先进磁共振成像技术和机器学习的发展为脑膜瘤 P/R 的无创术前预测提供了新的见解,有助于实现脑膜瘤 P/R 的准确预测。本综述总结了目前关于常规磁共振成像、功能磁共振成像和机器学习在预测脑膜瘤 P/R 中的研究。我们进一步探讨了肿瘤微环境在脑膜瘤 P/R 中的意义,将影像学特征与肿瘤微环境联系起来,全面揭示肿瘤异质性,为未来的研究提供新的思路。

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