预测质子束疗法技术对当地晚期肺癌患者肺部毒性的影响,这些患者参加了质子协作小组前性临床试验
Gilmer Valdes1, Jessica Scholey1, Tomi F Nano1
1Department of Radiation Oncology, University of California, San Francisco, California.
International journal of radiation oncology, biology, physics
|November 24, 2023
概括
机器学习模型在质子疗法后预测肺癌毒性. 像笔光束扫描和喘息等技术显著降低了肺炎或呼吸障碍的风险.
科学领域:
- 在瘤学瘤学.
- 医学物理 医学物理
- 辐射疗法 辐射疗法
背景情况:
- 接受质子束疗法 (PBT) 的肺癌患者面临肺毒性风险.
- 预测等级≥2肺炎或呼吸障碍的可能性对于治疗规划至关重要.
研究的目的:
- 开发和验证机器学习模型,以预测PBT治疗的局部晚期肺癌患者的肺毒性.
- 确定影响肺炎或呼吸障碍发展的关键因素.
主要方法:
- 对接受PBT的965名肺癌患者的人口统计和治疗数据的分析.
- 实现梯度增强,增材树和具有交叉验证的后勤回归模型.
- 计算平衡精度和曲线下的面积 (AUC) 用于模型性能评估.
主要成果:
- 在将人口统计和剂量计特征结合起来时,梯度增强模型实现了0.75±0.02的AUC.
- 笔光束扫描与显著较低的毒性有关 (0.08对0.34的概率).
- 腹部压缩,喘息和较低的双侧肺部剂量可以防止毒性.
结论:
- 先进的机器学习可以有效地预测使用PBT治疗的肺癌患者的肺毒性.
- 笔光束扫描,腹部压缩和对正常肺组织的剂量优化是减少毒性的关键.
- 发现模型的准确性取决于训练观察次数.
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