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Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

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临床可解释的机器学习模型用于预测心脏平均剂量,使用简单的基于BEV的指标.

Sathiyaraj Palanivel¹

¹Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, Karnataka 560029, India.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists

|February 22, 2026

概括

此摘要是机器生成的。

机器学习准确地预测乳腺癌放射治疗中的平均心脏剂量 (MHD),使用简单的光束眼视 (BEV) 度量. 这允许早期选择心脏节约技术,以减少辐射诱导的心脏毒性.

关键词:

梁的眼睛的视图.乳腺辐射疗法乳腺辐射疗法几何预测器的几何预测器线性回归是一种线性回归.后勤回归的逻辑回归心脏的平均剂量.

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科学领域:

辐射瘤学辐射瘤学
医学物理医学物理
医疗保健中的机器学习

背景情况:

辐射诱导的心脏毒性是左侧乳腺癌放射治疗中的一个重大问题.
平均心脏剂量 (MHD) 是长期心脏病发病率的关键预测指标.
准确预测MHD对于实施心脏节约策略至关重要.

研究的目的:

开发一种可临床解释的机器学习模型,用于预测MHD.
为了利用基于简单的beam's-eye-view (BEV) 的心脏投影指标进行MHD预测.
为了能够快速进行治疗前评估和选择放射治疗技术.

主要方法:

对127名接受左乳房/胸壁放射治疗的患者进行了回顾性分析,并进行了关节上关节辐射.
从触角场测量心脏投射 (水平和垂直) 的方法.
使用交叉验证开发多变量线性回归和逻辑回归模型.

主要成果:

线性回归模型显示出强大的预测性能 (R2=0.69,RMSE=0.61 Gy) 和显著的相关性 (r=0.83,p<0.001).
独立验证进一步提高了预测准确度 (R2=0.76,r=0.90,p<0.001).
在基于剂量的技术选择中,物流分类器实现了88%的准确性和高分辨率 (AUC=0.95).

结论:

基于BEV的简单心脏投射指标可以在左侧乳腺癌放射治疗中可靠地预测MHD.
开发的机器学习模型为预处理评估提供了一个实用的工具.
这种方法支持早期选择节约心脏的放射治疗技术,可能减少心脏病发病率.