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

Abstract

Meningiomas are the most common primary central nervous system tumors for which the preferred treatment is maximum safe resection, and the heterogeneity of meningioma results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma, which is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and even death, and 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 (MRI) technology and Machine Learning (ML) 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 of conventional MRI, MRI functional 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.