通过扩散放射学推进非侵入性质瘤分类:探索信号强度正常化的影响
Martha Foltyn-Dumitru1,2, Marianne Schell1,2, Felix Sahm3,4
1Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
Neuro-oncology advances
|April 10, 2024
概括
扩散权重成像 (DWI) 放射学增强了质瘤的预测,特别是在IDH野生型瘤中. 显微扩散系数 (ADC) 地图的规范化提高了模型的概括性,N4/z-score产生了最好的结果.
科学领域:
- 神经成像是一种神经成像.
- 在瘤学瘤学.
- 无线电学 (Radiomics) 是一种无线电学.
背景情况:
- 质瘤根据分子状况 (IDH突变,1p/19q共选择) 进行分类.
- 准确预测质瘤亚型对于治疗规划至关重要.
- 放射学和先进的MRI技术为非侵入性质瘤分类提供了潜力.
研究的目的:
- 评估扩散权重磁共振成像 (DWI-MRI) 对基于放射性基质瘤预测的影响.
- 评估DWI强度正常化如何影响预测模型的概括性.
- 为了比较表面扩散系数 (ADC) 地图的不同规范化策略.
主要方法:
- 从549名扩散性质瘤患者的手术前MRI扫描中提取了放射性特征.
- 解剖MRI序列经历了N4-偏差场校正和白条纹正常化.
- 表面扩散系数 (ADC) 地图使用N4或N4/z-score进行了规范化.
- 机器学习算法被训练为多类预测质瘤亚型.
- 在UCSF-glioma数据集 (n=409) 上进行了外部验证.
主要成果:
- 纯粹贝叶斯算法在内部测试集中表现最好.
- 纳入ADC放射学显著改善了IDH野生型质瘤预测曲线下的面积 (AUC) (0.79到0.86).
- 外部验证证实了IDH野生型和IDH突变1p/19q非带质瘤与ADC放射性蛋白质的改善AUC,特别是N4/z-score正常化.
结论:
- 表面扩散系数 (ADC) 放射学可以增强传统的MRI-based放射学模型用于质瘤分类.
- 性能增强在IDH野生型质瘤中最显著.
- ADC地图强度规范化的实用性取决于上下文,N4/z-score显示出显著的好处.
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