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基于扩散MRI的连接学特性改善了脑干结质瘤中H3K27M突变的非侵入性预测

Ne Yang¹, Xiong Xiao², Guocan Gu²

¹Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3).

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|July 6, 2023

概括

这项研究开发了一种机器学习模型,使用扩散MRI (dMRI) 来预测脑干结质瘤 (BSG) 中的H3K27M突变. 该模型有效地识别高风险患者,帮助制定BSG个性化治疗策略.

科学领域:

神经成像是一种神经成像.
在瘤学瘤学.
机器学习机器学习

背景情况:

脑干质瘤 (BSG) 是一种侵袭性的儿科瘤.
H3K27M突变是BSG进展的关键驱动因素和治疗标.
准确预测H3K27M突变状态对于治疗规划至关重要.

研究的目的:

在BSG患者中开发H3K27M突变的个性化预测模型.
将扩散MRI (dMRI) 的大脑结构连接分析集成到预测模型中.
为了识别具有H3K27M突变高风险的BSG患者.

主要方法:

对133名BSG患者 (80名H3K27M突变) 的回顾性分析,进行了手术前常规MRI和dMRI.
从MRI中提取瘤放射学特征,从dMRI中提取全球连接学特征.
使用放射学和连接学开发机器学习模型,在27名患者的独立队列上进行验证.

主要成果:

在独立验证组中,放射学和连接学模型的组合实现了0.9136的AUC.
放射学和连接学签名被生成,导致了简化后勤模型.
从物流模型中导出的名图在验证队列中获得了0.8827的AUC.

结论:

扩散MRI (dMRI) 和连接学分析是预测BSG中H3K27M突变的宝贵工具.
开发的模型在识别高风险患者方面表现良好.
整合多模式MRI数据可以改善对BSG中H3K27M突变状态的预测.

关键词:

大脑的结构连接性大脑的结构连接性大脑干的质瘤.H3K27M 在线阅读机器学习机器学习无线电学 (Radiomics) 是一种放射学.扩散磁力共振成像 (MRI) 扩散

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