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用基于放射学的机器学习来预测ATRX状态的MRI序列的比较

Nabila Gala Nacul Mora¹, Burak Han Akkurt¹, Dilek Kasap¹

¹Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany.

Diagnostics (Basel, Switzerland)

|July 14, 2023

概括

这项研究表明,T2加权的MRI序列可以预测扩散性质瘤中的ATRX (α-thalassemia/mental retardation syndrome X-linked) 基因突变. T2w MRI是使用放射学进行非侵入性ATRX突变状态预测的最合适的序列.

科学领域:

神经瘤学神经瘤学
放射学放射学是指放射学
机器学习机器学习

背景情况:

ATRX (alpha-thalassemia/mental retardation syndrome X-linked) 是2021年世卫组织成人类型扩散性结质瘤分类中的一个关键分子标记物.
准确的ATRX突变状态的非侵入性预测对于质瘤管理至关重要.

研究的目的:

开发和验证基于放射学的机器学习模型,用于在扩散性质瘤中非侵入性预测ATRX突变状态.
为了确定这个预测任务的最佳MRI序列.

主要方法:

从组织学上确诊的质瘤患者的MRI数据 (T1w,T1w与对比,T2w,FLAIR) 的回顾性分析.
从用手划分的感兴趣区域提取放射学特征.
开发机器学习模型使用拉索回归与100倍交叉验证.

主要成果:

T2w MRI 序列显示为预测 ATRX 突变的最高适用性.
使用T2w图像的七个特征模型实现了平均AUC为0.831,平均精度为0.746,平均灵敏度为0.772,平均特异性为0.697.
发现FLAIR序列是最不适合ATRX突变预测的.

结论:

在常见的MRI序列中,T2w MRI序列是最有效的,用于使用放射学和机器学习在扩散性质瘤中非侵入性预测ATRX突变状态.
应用于T2wMRI的放射学模型为评估ATRX突变状态而没有侵入性程序提供了一个有希望的方法.

关键词:

ATRX ATRX 在线观看这就是为什么MRI是MRI.人工智能的人工智能是人工智能.质瘤质瘤是一种神经成像是一种神经成像.无线电学 (radiomics) 是一种无线电学.

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