脑膜瘤复发:是时候推出在线预测工具了?

Abdulrahman Albakr, Amir Baghdadi, Brij S. Karmur, S. Lama, Garnette R. Sutherland
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引用次数: 0

摘要

脑膜瘤是最常见的脑肿瘤,传统上被认为是良性的,但在患者的一生中复发的风险相对较高。此外,随着一些临床、放射学和分子变量的出现,现有的分级标准,包括辛普森分级和世界卫生组织分级,显然可能不够充分或准确。随着可广泛访问和使用的网络工具(如用于基因鉴定或其他癌症的工具)的普及,脑膜瘤治疗应及时利用不断发展的新标记物来帮助推进患者治疗。我们回顾了2022年9月至2023年12月期间的原创研究和综述文章,这些研究和文章提供了成人脑膜瘤的人口统计学、临床、放射学、组织病理学、分子遗传学和管理方面的最新信息。我们的范围回顾揭示了大量脑膜瘤文献,这些文献评估了脑膜瘤复发和侵袭性肿瘤生物学的决定因素,包括年龄较大、女性、端粒酶逆转录酶启动子突变、CDKN2A缺失、次全切除和较高分级等遗传异常。尽管有大量关于脑膜瘤的证据,但我们注意到缺乏帮助临床医生做出决策的工具。我们发现需要一种在线、可自我更新、基于机器学习的动态模型,该模型可结合人口统计学、临床、放射学、组织病理学和遗传学变量来预测脑膜瘤的复发风险。尽管这是一项具有挑战性的工作,但脑膜瘤复发预测工具将为脑膜瘤患者和临床医生提供重要信息,帮助他们做出长期监测和管理脑膜瘤的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Meningioma recurrence: Time for an online prediction tool?
Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient’s lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson’s and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, CDKN2A deletion, subtotal resection, and higher grade. Despite a large body of evidence on meningiomas, however, we noted a lack of tools to aid the clinician in decision-making. We identified the need for an online, self-updating, and machine-learning-based dynamic model that can incorporate demographic, clinical, radiographic, histopathological, and genetic variables to predict the recurrence risk of meningiomas. Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.
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