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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Three-Compartment Open Model01:06

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

Determination of Multiple Dosing Parameters: Loading and Maintenance Doses

A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用多任务深度学习模型进行多模式放射治疗剂量预测.

Austen Maniscalco1, Ezek Mathew1, David Parsons1

  • 1Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Medical physics
|May 6, 2024
PubMed
概括
此摘要是机器生成的。

一个多任务的深度学习模型有效地预测了辐射疗法剂量分布,用于跨多种模式的加速部分乳房照射. 这种方法有助于个性化治疗决策,并优化临床工作流程.

关键词:
人工智能的人工智能是人工智能.乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.剂量预测剂量预测模式比较 模式比较多任务多任务.辐射治疗疗法 辐射治疗疗法

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 加快部分乳房照射 (APBI) 优先于全乳房照射,因为其向剂量和治疗时间更短.
  • 存在各种APBI模式,每个都有独特的剂量分配特征,需要针对患者的最佳模式选择.
  • 对于每种疗法而言,手动治疗规划是耗时且在临床上不切实际的.

研究的目的:

  • 通过深度学习开发一种高效,个性化的方法来选择APBI的最佳辐射疗法 (RT) 模式.
  • 训练一个能够同时预测各种RT模式的剂量分布的多任务 (MT) 网络.
  • 提供量化,患者特定的剂量洞察力,以便在治疗计划之前进行知情的模式比较.

主要方法:

  • 使用了28名APBI患者和92个治疗计划的数据集,分为培训,验证和测试集.
  • 单任务 (ST) 模型被训练为每个模式,并与一个单一的MT模型预测所有模式的剂量.
  • 用测试数据集的平均绝对百分比误差 (MAPE) 评估模型性能,并通过Wilcoxon签名等级测试进行统计分析.

主要成果:

  • MT模型需要2384分钟的训练,而五种ST模型总共需要1925分钟.
  • MT模型的预测平均为每位患者1.82秒,而ST模型的每种模式为0.93秒.
  • 在MT模型中,MAPE (1.1033 ± 0.3627%) 比集体ST模型 (1.2386 ± 0.3872%) 低得多.

结论:

  • 多任务学习框架可以在没有重大妥协的情况下有效地预测跨模式的RT剂量分布.
  • MT架构提供了灵活性,可扩展性和简化管理,使其适合临床部署.
  • 这种方法提高了患者的决策能力,为医生提供了定量见解,并优化了诊所资源配置.