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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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基于物理的机器学习,用于预测VMAT中的MLC和门架错误:一个特征工程方法.

Perumal Murugan1, Ravikumar Manickam1

  • 1Sri Shankara Cancer Hospital and Research Centre Bengaluru, Karnataka, India.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
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概括

基于物理的机器学习准确地预测了体积调制弧线疗法 (VMAT) 交付中的错误. 功能工程显著减少了位置错误,改善了放射治疗质量保证.

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特性工程是指特征工程.摩擦因子是一个摩擦因子.机器学习就是机器学习.优化Optuna的优化方法预测建模的预测建模.在SHAP分析中,我们分析了SHAP.

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科学领域:

  • 医学物理 医学物理
  • 机器学习在放射治疗中的应用
  • 辐射瘤学 辐射瘤学

背景情况:

  • 卷度调制弧线疗法 (VMAT) 涉及复杂的交付动态.
  • 准确地预测多叶合器 (MLC) 和门架位置错误对于VMAT质量保证至关重要.
  • 现有的方法可能无法完全捕捉VMAT交付的复杂物理.

研究的目的:

  • 开发一个基于物理的功能工程机器学习 (ML) 框架,用于预测VMAT中的MLC和门架位置错误.
  • 引入新的基于物理的参数,以提高预测准确度.
  • 为了比较不同的ML模型对VMAT错误预测的性能.

主要方法:

  • 使用了VMAT轨迹日志和DICOMRT计划,这些日志来自32个TrueBeam linac处理和HD120 MLC.
  • 提取了交付动态和基于物理的工程特征 (摩擦,重力,MLC速度正常化).
  • 使用Optuna训练和优化XGBoost,LightGBM和深度神经网络 (DNN);通过Spearman相关性,相互信息和SHAP评估特征的重要性.

主要成果:

  • 在DICOM-RT和轨道日志数据之间发现了系统的差异 (7-8.5%的偏差).
  • MLC速度是主要的预测因素 (rs=0.891);物理驱动的特征显示了显著的相关性.
  • 轻GBM和XGBoost实现了优越的MLC错误预测 (MAE:0.0019毫米),减少了30%的剩余错误;DNN的性能不那么有效.
  • 格兰特错误预测的准确性较低 (MAE:0.012°-0.015°).

结论:

  • 在ML中集成领域知识显著增强了放射治疗应用.
  • 基于物理的特征工程实现了30%的减少VMAT的位置错误.
  • 建议在VMAT质量保证中优先考虑功能空间探索与模型优化.