彻底改变宫癌护理:高热和机器学习的协同效应
Shifang Feng1, Huixia Wang2, Xiaoyu Duan1
1Gansu Provincial Hospital, Lanzhou, Gansu Province, China.
概括
加热与化疗放射治疗 (CCRT) 结合,改善了宫癌患者的免疫标志物,尽管生存结果需要更长的随访. 机器学习模型有效地根据这些免疫变化预测了治疗疗效.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 医学物理 医学物理
背景情况:
- 局部晚期宫癌 (LACC) 治疗通常涉及并发化疗放射治疗 (CCRT).
- 超热症正在作为一种辅助疗法来提高CCRT的疗效.
- 外周免疫标记可以反映系统免疫状态和治疗反应.
研究的目的:
- 为了研究高热加CCRT对LACC患者周围免疫标记物的影响.
- 开发和验证机器学习模型,以基于免疫标记物的动态来预测高热症的疗效.
主要方法:
- 60名LACC患者被分为观察组 (CCRT+高温) 和对照组 (单独使用CCRT).
- 周围免疫标记物,包括全血细胞计数,T淋巴细胞子集 (CD3+T,CD4+T,CD8+T) 和细胞因子 (IL-1β,IL-6,IL-8,TNF-α),在治疗前和后进行测量.
- 奇平方测试和机器学习算法 (随机森林,多层感知器) 用于特征选择和模型开发.
主要成果:
- 没有观察到两年无进展生存 (PFS) 或总生存 (OS) 的显著差异,可能是由于随访时间短.
- 与对照组相比,高温组在治疗后显示出明显较低的中性粒细胞与淋巴细胞比率 (NLR),单细胞与淋巴细胞比率 (MLR),血小板与淋巴细胞比率 (PLR),IL-8和CD8+T水平.
- 相反,TNF-α,IL-1β,IL-6,CD3+T,CD4+T和T细胞比率 (CD3+T/CD8+T,CD4+T/CD8+T) 在高温组显著增加.
- 随机森林和多层感知器模型在使用选择的免疫特征预测治疗疗效方面表现最佳.
结论:
- 加热结合CCRT调节周围免疫指标,可能改善LACC患者的全身免疫微环境.
- 动态外周免疫标志物是高温症在LACC治疗中的有效性有价值的预测因素.
- 机器学习模型,特别是RF和MLP,显示出基于免疫配置文件预测治疗结果的前景.
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