在个性化超分片立体自适应放射疗法 (PULSAR) 中基于多组学的结果预测
Haozhao Zhang1,2, Michael Dohopolski1,2, Strahinja Stojadinovic1
1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Cancers
|October 16, 2024
概括
这项研究开发了一种集成放射学,剂量学和三角形特征的多组学方法,以预测经过脉冲射频治疗 (PULSAR) 的大脑转移 (BM) 患者的治疗反应,实现高准确度.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医学成像分析 医学成像分析
背景情况:
- 预测大脑转移 (BM) 治疗反应对患者管理至关重要.
- 脉冲射频 (PULSAR) 是一种治疗BM的方法.
- 整合不同的数据类型可以提高预测准确性.
研究的目的:
- 开发和评估一种多组学方法,用于预测经过PULSAR治疗的BM患者的治疗反应.
- 整合放射学,剂量学和三角形特征,以提高预测.
- 评估集合特征选择 (EFS) 模型的性能.
主要方法:
- 对39名用PULSAR治疗的BM患者 (69个病变) 的回顾性分析.
- 从治疗前和治疗期间的MRI和剂量分布中提取放射性,剂量和三角形特征.
- 对六个个别模型和一个EFS模型进行评估,以预测>20%的体积减少.
主要成果:
- 集成多经济学特征的EFS模型超过了单个模型的表现.
- 在EFS模型中,AUC达到0.979,准确度为0.917,F1得分为0.821.
- 顶级预测功能包括后波形变形和原始图像功能.
结论:
- 数据驱动的多组学方法是可行的,用于预测PULSAR治疗结果在BM.
- 将多组学与治疗内决策支持相结合,可以优化患者管理.
- 这种方法可以降低BM患者治疗不足或过度的风险.
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