相关实验视频

Updated: May 1, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

在放射治疗治疗计划优化中嵌入基于机器学习的毒性模型

Donato Maragno¹, Gregory Buti², Ş İlker Birbil¹

¹Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands.

Physics in medicine and biology

|February 27, 2024

概括

这项研究开发了一个个性化的放射治疗框架,使用优化与约束学习来减少辐射诱导的毒性. 这种方法显著降低了肺癌患者辐射肺炎的风险,而不会影响瘤覆盖率.

科学领域:

医学物理医学物理
辐射瘤学辐射瘤学
计算生物学计算生物学

背景情况:

放射治疗 (RT) 由于辐射诱导毒性 (RIT) 带来了挑战.
个性化治疗计划对于尽量减少不良副作用至关重要.
现有的方法往往缺乏对毒性预测和治疗优化之间的整合.

研究的目的:

开发和评估一个个性化的放射治疗治疗计划框架,使用优化与约束学习 (OCL).
利用患者特异性数据和机器学习来预测和减轻非小细胞肺癌 (NSCLC) 患者的辐射肺炎 (RP2+).
减少RIT,同时保持RT的目标覆盖率.

主要方法:

实施了三步OCL框架:基线计划优化,基于ML的RIT预测和患者特定约束适应.
利用分类树,集合方法和神经网络来预测RP2+概率.
评估了四名高风险NSCLC患者的方法,将OCL增强计划与传统计划进行了比较.

主要成果:

在OCL框架成功地将预测模型整合到RT规划中.
平均肺剂量和V20减少,导致RP2+平均风险降低,从95%降至42%.
维持瘤覆盖,在某些情况下,脊髓最大剂量略有增加.

结论:

该OCL框架有效地降低了NSCLC患者的辐射肺炎风险.
将患者特异性数据整合到学习约束中,可以提高个性化的RT决策.
这种方法弥合了毒性预测和治疗优化之间的差距,以改善患者的治疗结果.

关键词:

在NSCLCLC中,我们可以看到.约束学习学习的限制机器学习是机器学习.优化的优化优化优化.个性化治疗计划个性化治疗计划辐射性肺炎的发生辐射诱导的毒性引起的毒性.

更多相关视频

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

相关概念视频

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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 Criterion

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.

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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...

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...