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化学辐射治疗后非喉头癌的声带功能障碍:使用CT放射学和机器学习进行预测建模.

Sakineh Bagherzadeh1, Pedram Fadavi2, Hamid Abdollahi3,4

  • 1Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran, semums.ac.ir.

BioMed research international
|December 1, 2025
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概括

使用CT放射性特征和临床数据的机器学习模型可以预测化学辐射治疗后头癌患者的声带功能障碍,从而改善结果预测.

关键词:
化学辐射疗法是一种化学辐射疗法.机器学习是机器学习.无线电学 (radiomics) 是一种无线电学.声带功能障碍 声带功能障碍

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科学领域:

  • 放射学和瘤学 放射学和瘤学
  • 医学成像分析 医学成像分析
  • 医疗保健中的机器学习

背景情况:

  • 用化学辐射疗法 (CRT) 治疗的头癌 (HNC) 患者有发展声带功能障碍 (VCD) 的风险.
  • 预测VCD对于管理治疗副作用和改善患者生活质量至关重要.
  • 当前的预测方法可能无法完全捕捉到导致VCD的因素的复杂相互作用.

研究的目的:

  • 调查计算机断层扫描 (CT) 放射性特征和VCD的剂量测量临床生物标志物的预测价值在接受CRT的HNC患者中.
  • 开发和评估用于预测辐射诱导VCD的机器学习 (ML) 模型.

主要方法:

  • 分析了65名接受CRT治疗的HNC患者.
  • 收集了CT放射性特征,临床数据和剂量-体积组图 (DVH) 度量.
  • 九个ML分类器使用特征选择算法 (LASSO,额外树,弹性网) 在仅放射学和组合数据集上进行训练.

主要成果:

  • 放射学模型显示了适度的预测性能 (例如,随机森林AUC为0.84).
  • 结合了放射性,临床和剂量学特征的组合模型,通过LASSO和弹性网实现了高预测准确性 (AUC>0.95).
  • 特性选择算法对组合模型的性能产生了重大影响.

结论:

  • 预处理CT放射性特征作为有效的生物标志物用于预测像VCD这样的辐射诱导毒性.
  • 将放射性特征与临床和剂量测量数据相结合,显著提高了放射治疗结果的预测建模.
  • 这些发现支持使用ML进行个性化放射治疗规划和HNC中毒性管理.