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基于MRI的放射学用于对质瘤中的分子生物标志物的非侵入性预测

Edoardo Agosti¹, Karen Mapelli¹, Gianluca Grimod²

¹Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy.

|February 13, 2026

概括

放射学显示出预测质瘤分子生物标志物的前景,使用MRI数据进行非侵入性预测. 然而,临床使用受到不一致的方法和缺乏标准化的限制.

科学领域:

放射学和医学成像分析分析.
神经瘤学和精密医学领域.
机器学习在医疗保健中的应用

背景情况:

放射学提供了使用定量MRI数据的质瘤分子格局的非侵入性特征.
支持精确瘤学通过预测基因组,表观遗传和表型变化的无需侵入性采样.
系统审查综合了放射学应用,用于非侵入性脑瘤生物标志物预测.

研究的目的:

系统地审查放射学应用,以对质瘤中的分子生物标志物的非侵入性预测.
评估当前的方法趋势,性能指标和放射学在质瘤研究中的翻译准备.

主要方法:

在PubMed,Ovid MEDLINE和Scopus中按照PRISMA 2020指南进行系统的文献搜索.
从70项符合条件的MRI序列,细分,计算方法和生物标志物研究 (10,324名患者) 中提取数据.
使用放射学质量评分 (RQS),IBSI标准和纽卡斯尔-太华尺度 (NOS) 评估的方法质量.

主要成果:

IDH突变是预测最多的生物标志物 (70%的研究),其次是ATRX (38.6%).
报告的高诊断性能 (AUC 0.71-0.99),深度学习/混合模型显示出优异的结果.
在成像协议,细分和报告中存在显著的异质性,并且不一致地遵守标准化标准.

结论:

放射学对高诊断准确度的非侵入性质瘤分子生物标志物预测具有很大的潜力.
临床翻译受到异质的协议,缺乏标准化,不充分的验证和可变的方法严谨性所阻碍.

关键词:

深度学习是一种深度学习.质瘤质瘤是一种机器学习是机器学习.磁共振成像技术的使用分子生物学分子生物学无线电学 (radiomics) 是一种无线电学.系统性审查系统性审查

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