预测中风严重程度:对病变表示的系统评估
Anna K Bonkhoff1, Alexander L Cohen2,3, William Drew3
1J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Annals of clinical and translational neurology
|October 12, 2024
概括
预测中风缺陷需要大型的多中心数据集. 与损伤体积或位置相比,结构性和功能性大脑脱节显著改善了中风严重程度的预测.
科学领域:
- 神经成像是一种神经成像.
- 中风研究研究中风研究
- 神经学赤字预测预测的神经学赤字预测
背景情况:
- 精确预测中风后缺陷对于患者管理至关重要.
- 当前的预测模型往往缺乏跨不同数据集的概括性.
- 确定可靠的成像生物标志物来确定中风的结果是一个持续的挑战.
研究的目的:
- 系统地评估基于病变的成像特征,以预测中风后的缺陷.
- 确定在独立数据集中统计预测中风严重程度的最佳方法.
- 评估数据集大小和来源 (单中心与多中心) 对预测准确性的影响.
主要方法:
- 利用来自三个独立的急性中风数据集 (N=109,638,794) 的成像和临床数据.
- 统计学预测的急性中风严重程度 (NIHSS) 使用病变体积,位置和结构/功能断开,来自规范连接组.
- 在不同的数据集大小和培训策略中比较预测性能 (数据集内交叉验证与独立数据集测试).
主要成果:
- 在数据集内的交叉验证显示出良好的性能,但对独立数据集的概括性较差 (中位数R2=0.2%).
- 较大的单中心培训数据 (R2=15.8%) 和更多的多中心培训数据 (R2=24.4%) 提高了预测准确性.
- 纳入结构性或功能性大脑断开连接的模型显著优于仅基于损伤体积或位置的模型 (P<0.001).
结论:
- 数据集中的交叉验证不能可靠地预测独立数据集的性能.
- 在大型多中心数据集上训练预测模型对于可概括的结果至关重要.
- 结构性和功能性大脑脱节是急性中风严重程度的强大预测因素.
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