通过可解释机器学习通过放射治疗后恶性间皮瘤患者预测整体存活率
Zitian Wang1, Vincent R Li2, Fang-I Chu1
1Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Cancers
|August 12, 2023
概括
这项研究开发了机器学习模型,以预测用放射治疗治疗的恶性多间皮瘤患者的整体存活率. 临床和剂量测量因素显著影响存活率,有助于个性化治疗计划.
科学领域:
- 在瘤学瘤学.
- 辐射疗法 辐射疗法
- 机器学习 机器学习
背景情况:
- 恶性多层层层瘤 (MPM) 是一种具有不良预后的侵袭性癌症.
- 放射治疗是MPM的关键治疗方式.
- 预测生存结果对于个性化治疗至关重要.
研究的目的:
- 开发MPM患者接受放射治疗的整体生存预测模型.
- 使用可解释的机器学习 (ML) 方法.
- 确定影响生存的关键剂量计和患者特征.
主要方法:
- 对60名MPM患者进行了回顾性分析,这些患者接受了45 Gy放射治疗.
- 应用多变量考克斯回归 (Cox PH) 和生存支持矢量机器 (sSVM).
- 评估临床,剂量计和组合变量,以预测存活率.
主要成果:
- 剂量测量终点根据瘤横向性 (左与右) 有显著差异.
- 左侧和右侧MPM患者之间整体生存 (OS) 没有显著差异 (p=0.18).
- 确定了所有原因死亡风险的特定剂量测量 (例如,PTV_Min,Contra_Lung_V20) 和临床 (例如,N阶段,男性性别) 预测因素.
结论:
- 临床和剂量测量变量可以预测中皮瘤患者的整体存活率.
- 这些预测因素可以指导个性化放射治疗计划,以改善治疗反应.
- 结果为MPM管理中的临床实践提供了翻译价值.
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