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基于分解的FDG PET的表型异质性预测叶患者的临床预后和决策

Kun Guo¹, Zhiyong Quan¹, Guiyu Li¹

¹Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

|March 8, 2024

概括

这项研究使用了18F-FDG PET扫描的贝叶斯模型,在叶 (TLE) 患者中发现了三种不同的葡萄糖代谢模式. 这些模式有助于预测的负担和治疗反应.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
计算生物学计算生物学

背景情况:

叶 (TLE) 在临床表现和治疗反应方面表现出显著的异质性.
了解TLE潜在的神经生物学变异对于个性化医学至关重要.

研究的目的:

应用数据驱动的贝叶斯模型,从TLE患者的18F-FDG PET扫描中识别出不同的潜在疾病因素.
分析这些已识别的因素与病负担 (发作发作,持续时间,频率) 之间的关系.
开发用于临床预后和决策的预测模型.

主要方法:

在70名TLE患者的18F-FDG PET扫描上利用无监督机器学习来估计潜在的疾病因素.
量化了个体参与者内多个不同的因素的表达.
开发监督分类器,用于预测抗药物 (AED) 反应和手术后发作结果.

主要成果:

确定了三个关键的潜伏因素:单边的时间/海马体低代谢,双边的前额叶低代谢和双边的时间低代谢.
因素3 (双边时性低代谢) 与发作年龄和持续时间有显著的负相关性.
预测模型在术后发作结果 (0.857) 和临床决策 (0.965) 中实现了高AUC.

结论:

在TLE中基于18F-FDG PET的表型异质性的分解是可行的.
这种方法有助于对疾病监测进行个体级预测.
能够为TLE管理制定个性化治疗策略.

关键词:

18F-FDG 皮革皮革 (PET) 的使用葡萄糖的新陈代谢.现象型异质性的异质性时间叶发作时间叶.

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