开放式放射学:标准化数据集的集合和可复制的放射学机器学习管道的技术协议
Khashayar Namdar1,2,3,4, Matthias W Wagner1,2,5,6, Birgit B Ertl-Wagner1,2,5
1Department of Diagnostic & Interventional Radiology, The Hospital for Sick Children (SickKids), Toronto, ON, Canada.
BMC medical imaging
|August 5, 2025
概括
放射学可重现性受到特征提取变异性的挑战. 瘤子区域和成像序列显著影响模型性能,一些完美的结果被证明是不可重现的.
科学领域:
- 机器学习在医学成像中的应用.
- 放射学及其应用.
- 科学研究中的可复制性
背景情况:
- 放射学是医学成像中的机器学习的一个关键组成部分,在可复制性和可访问性方面面临重大挑战.
- 研究放射学特征提取对结果可重现性的影响对于临床采用至关重要.
研究的目的:
- 引入开放式放射学,一个新的放射学数据集和一个全面的管道.
- 调查放射学特征提取对结果可重复性的影响,使用拟议的技术协议.
主要方法:
- 来自开源源 (BraTS 2020,BraTS 2023,TCIA) 的大型放射学数据集,用于质瘤和非小细胞肺癌研究.
- 利用BraTS 2020 MRI数据集创建了288个放射学数据集,通过不同的MRI序列,容器宽度,正常化方法和瘤子区域来创建.
- 在每个数据集上执行了100倍的交叉验证与随机森林分类器,以通过接收器操作特征曲线 (AUROC) 下的区域评估可重复性.
主要成果:
- 瘤子区域和成像序列是影响模型性能的重要因素,与 bin 宽度和图像正常化不同.
- T1对比增强序列和瘤亚区域的特定组合产生了最高的平均测试AUROC (0.951).
- 虽然一些设置实现了完美的AUROC分数 (1),但这些结果被发现是不可重现的.
结论:
- 放射学管道的变化,特别是关于瘤亚区域选择的变化,可以显著影响结果.
- 在放射学研究中,表面上的完美表现可能来自不可重现的因素,这凸显了对强大的方法学的需求.
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