改善辐射肺炎的预测:在IDLSS肺部子区域内利用放射学和剂量学
Tsair-Fwu Lee1,2,3, Wen-Ping Yun1,4, Ling-Chuan Chang-Chien1
1Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
Life (Basel, Switzerland)
|February 27, 2026
概括
这项研究预测了使用先进的剂量学和放射学在VMAT后肺癌患者的辐射性肺炎 (RP) 风险. 将这些特征结合起来,显著提高了预测准确度,超过了传统的剂量-体积直方体指标.
科学领域:
- 医学物理 医学物理
- 放射治疗瘤学 放射治疗瘤学
- 计算生物学 计算生物学
背景情况:
- 辐射性肺炎 (RP) 是肺癌放射治疗中显著的剂量限制毒性.
- 准确预测RP风险对于优化治疗计划和改善患者结果至关重要.
- 传统的剂量-体积直方图 (DVH) 度量在捕捉复杂的空间剂量分布方面存在局限性.
研究的目的:
- 在肺癌患者的VMAT后开发和验证RP风险的预测模型.
- 在增量剂量间隔基础肺部亚区域 (IDLSS) 中研究高维剂量学和放射学特征的实用性.
- 将剂量学和放射学与传统的DVH指标的预测性能进行比较.
主要方法:
- 对136名接受VMAT治疗的肺癌患者的回顾性分析.
- 划分了七个感兴趣的区域 (ROI),包括IDLSS定义的肺部次区域.
- 从ROI中提取DVH,放射学和剂量学特征.
- 应用LASSO用于特征选择和SMOTE用于类不平衡.
- 开发和交叉验证后勤回归,随机森林和神经网络模型.
主要成果:
- 特定剂量范围内的身体质量指数 (BMI) 和肺部亚区域体积 (5-10 Gy, 10-20 Gy) 是显著的RP预测因素.
- 与DVH特征相比,dosiomics特征表现出优越的预测性能.
- 一个结合放射学和剂量学模型实现了最高的预测准确性 (AUC=0.91).
- 在不影响特异性的情况下,SMOTE显著提高了模型灵敏度.
结论:
- 从IDLSS定义的肺部亚区域整合放射学和剂量学可以提高VMAT后RP预测的准确性.
- 剂量学特征对于评估空间剂量异质性和RP风险至关重要.
- 这种方法为肺癌的个性化放射治疗规划提供了更精确的方法.
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