确定急性缺血性中风发病时间,使用机器学习和心脏病发作病变和整个大脑的放射学特征
Jiaxi Lu1,2, Yingwei Guo2,3, Mingming Wang4
1School of Applied Technology, Shenzhen University, Shenzhen 518060, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
准确的急性缺血性中风 (AIS) 发病时间预测对于患者的康复至关重要. 结合心脏病发作病变和整个大脑的放射性特征,可以显著提高预测准确度.
科学领域:
- 神经成像是一种神经成像.
- 无线电学 (Radiomics) 是一种无线电学.
- 脑卒中医学 脑卒中医学
背景情况:
- 精确确定中风发病时间对于有效治疗和急性缺血性中风 (AIS) 患者康复至关重要.
- 现有的方法可能无法充分利用整个大脑中可用的综合信息来预测发病时间.
研究的目的:
- 为了提高预测急性缺血性中风 (AIS) 发病时间的准确性.
- 为了研究放射学特征从心脏病发作病变和整个大脑的联合实用性.
主要方法:
- 从心脏病发作病变和整个大脑中提取放射性特征,使用明显扩散系数 (ADC),扩散加权成像 (DWI) 和流体减弱倒置恢复 (FLAIR) 序列.
- 对于特征选择,使用了绝对最小的收缩和选择操作员 (Lasso).
- 评估了四种特征组合策略:心脏病变 (IL),全脑 (WB),直接组合 (IW) 和拉索选择组合 (IWS).
主要成果:
- IWS策略结合并选择了来自心脏病发作病变和整个大脑的特征,实现了最高的曲线下的面积 (AUC) 0.904.
- 这是一个显著的改善:13.5%比IL (0.769),18.7%比WB (0.717) 和4.2%比IW (0.862).
- 十倍的交叉验证证实了IWS方法的优越预测性能.
结论:
- 整合来自心脏病发作病变和整个大脑的放射学特征可以提高AIS发病时间预测的准确性.
- 提出的方法,特别是拉索特征选择 (IWS),为AIS管理中的临床决策提供了更精确的工具.
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