在产前MRI中的机器学习可以预测分离的腹腔大的胎儿产后腹腔异常
Xue Chen1, Daqiang Xu1, Xiaowen Gu1
1Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou City, Jiangsu Province, 215002, China.
European radiology
|May 10, 2024
概括
胎儿MRI上的皮质下放射学可以预测胎儿腹腔巨 (FVs) 的胎儿的产后侧室变化. 这种方法可以减少对重复产前成像的需要.
科学领域:
- 神经成像是一种神经成像.
- 无线电学 (Radiomics) 是一种辐射学.
- 胎儿医学 胎儿医学
背景情况:
- 胎儿腹腔巨变 (FV) 是一种影响产后神经发育的疾病.
- 预测FVs的产后侧腔室变化对于管理至关重要.
- 目前的方法可能需要重复产前成像.
研究的目的:
- 评估正常胎儿 (NFs) 和FVs的内结构和大脑帕伦基马放射学.
- 用放射学来预测FVs的产后侧心室变化.
- 评估放射学在区分NF和FV方面的有效性.
主要方法:
- 在141个NF和101个FV上进行了1.5T MRI.
- 用5倍交叉验证的物流回归模型用于预测.
- 模型性能使用ROC曲线,校准曲线和DCA进行验证.
主要成果:
- 在NF和FV之间发现了显著的大脑结构改变 (p <0.05).
- 皮层下放射学有效地将NF与FV区分开来 (AUC = 1/0.992).
- 皮下放射学模型预测了FV的侧腔室变化,AUC为0.822/0.743.
结论:
- 皮层下膜的微观结构变化预测了FVs的产后侧腔室变化.
- 用放射学预测单个胎儿MRI可以预测产后变化,可能减少重复成像.
- 机器学习和皮层下放射学为预测胎儿腹腔大的结果提供了一个有希望的方法.
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