非线性混合效应建模和基于人群的模型选择131在良性甲状腺疾病中的I动力学
Deni Hardiansyah1, Ade Riana1, Heribert Hänscheid2
1Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
EJNMMI physics
|April 8, 2025
概括
使用基于人群的模型选择和非线性混合效应 (PBMS-NLME) 的新数学模型准确计算了I治疗的时间集成活动 (TIA). 这种改进的模型提高了对治疗良性甲状腺疾病的剂量计的精度.
科学领域:
- 核医学是一种核医学.
- 医学物理 医学物理
- 药理动力学 药理动力学
背景情况:
- 放射性 (I) 治疗对于治疗良性甲状腺疾病至关重要.
- 精确的剂量测量,特别是时间整合活动 (TIAs),对于有效和安全的I治疗至关重要.
- 目前的剂量计模型可能需要改进以提高准确性.
研究的目的:
- 开发和验证一种新的数学模型,用于在I治疗中精确计算TIA.
- 为了利用基于人口的模型选择和非线性混合效应 (PBMS-NLME) 方法进行这一开发.
- 将新模型的性能与现有标准进行比较,例如欧洲核医学协会 (EANM) 标准操作程序 (SOP).
主要方法:
- 收集了131I的生物动力学数据,来自73名患者,在多个时间点处方后.
- 采用PBMS-NLME建模来选择基于Akaike权重的最佳指数函数和 (SOEF).
- 评估了9个SOEF,包括EANM SOP函数,并重复安装以确保最佳的参数识别.
主要成果:
- 在PBMS-NLME分析中,确定了一个特定的SOEF为最佳模型,由大约100%的Akaike重量支持.
- 选择的PBMS-NLME模型在描述131I生物动力学方面表现出优异的表现,与EANM SOP函数与个体拟合相比.
- 来自PBMS-NLME的最佳SOEF包含了一个额外的免费参数.
结论:
- 使用PBMS-NLME开发的数学模型为I治疗中计算TIA提供了更高的准确性.
- 在PBMS-NLME模型中包含一个额外的参数有助于提高其预测能力.
- 这种精细的剂量测量方法有望优化良性甲状腺疾病的治疗结果.
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