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Eric E Sigmund¹, Gene Y Cho^1,2,3, Dibash Basukala^1,4

¹Center for Biomedical Imaging, Center for Advanced Innovation and Imaging Research (CAIIR), Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016.

Radiology. Imaging cancer

|September 5, 2025

概括

乳腺癌特征的转化潜力显示在不同MRI供应商和软件中具有很高的诊断精度. 这项技术为改善乳腺癌诊断提供了有前途的工具.

科学领域:

放射学
生物医学成像
癌症学

背景情况:

磁共振成像 (MRI) 提供了对组织微观环境的深入了解.
在不同MRI供应商和软件中对IVIM生物标志物的标准化对于临床翻译至关重要.
乳腺癌的特征依赖于准确和可复制的成像生物标志物.

研究的目的:

评估乳腺癌特征IVIM生物标志物的一致性和诊断性能.
来自不同MRI供应商和分析软件的IVIM指标进行比较.
评估IVIM的转化潜力,以区分良性与恶性乳腺病变.

主要方法:

从两个地点对106名患者的扩散权重成像 (DWI) 数据进行了回顾性分析.
使用来自两个供应商的1.5T和3TMRI扫描仪获取多个b值.
使用两个软件包 (Igor和Firevoxel) 来导出和比较IVIM参数 (Dt,fp,Dp) 和它们的放射学.
统计分析包括布兰德-阿尔特曼,后勤回归和ROC曲线分析以评估诊断性能.

主要成果:

组织扩散性 (Dt) 显示了跨站点的最高软件一致性.
各个IVIM指标如Dt,min,fp,max和Dp,max显示出显著的诊断值 (AUC在0. 75到0. 835.
结合五个关键IVIM指标的多变量模型实现了高诊断准确度 (AUC高达0.90±0.03).

结论:

IVIM生物标志物是可复制的,并且具有显著的转化潜力来表征乳腺癌.
该研究强调使用IVIM来区分良性和恶性乳腺病变的可行性.
跨不同平台的标准化IVIM分析可以提高乳房MRI的诊断能力.

关键词:

乳房用MR扩散加权成像技术评估

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