放射学和机器学习用于诊断质脑瘤:系统性审查和元分析
G V Danilov1, S B Agrba2, Yu V Strunina3
1MD, PhD, Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
Sovremennye tekhnologii v meditsine
|February 13, 2026
概括
放射学和机器学习在使用MRI扫描的脑质瘤亚型中显示出高精度. 然而,不一致的方法阻碍了临床应用,凸显了放射学程序标准化的需要.
科学领域:
- 神经瘤学神经瘤学
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 质瘤是最常见的原发性脑瘤.
- 准确的亚型对有效的治疗策略至关重要.
- 像神经成像和放射学这样的非侵入性诊断方法越来越重要.
研究的目的:
- 审查和分析放射学和机器学习在使用MRI数据诊断质瘤方面的挑战和质量.
- 评估这些技术在预测分子生物标记物状态方面的准确性.
主要方法:
- 系统的文献综述和对42篇出版物的元分析.
- 使用放射学和机器学习对MRI数据进行研究的分析,用于质瘤亚型化.
- 包括各种分子生物标志物:IDH,ATRX,BRAF,H3K27M突变,TERT促进子突变,1p/19q编解,MGMT甲基化和Ki-67指数.
主要成果:
- 高整体准确度为0.86 [0.83;0.89]用于放射学和机器学习,用于预测分子生物标志物状态.
- 在研究中观察到显著的方法异质性.
- 缺乏统一的标准来选择特征提取感兴趣的地区 (ROI) 是一个主要的挑战.
结论:
- 放射学和机器学习显示出对非侵入性质瘤诊断的前景.
- 方法上的异质性,特别是在ROI选择方面,阻碍了临床可重复性.
- 放射学程序的标准化对于未来的研究和临床转化至关重要.
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