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在IIB/IIC阶段黑色素瘤中辅助药尼沃卢马布与潘布罗利祖马布:基于强化学习的模拟研究

Perihan Perkin¹, Fahriye Tuğba Köş²

¹Department of Medical Oncology, Ankara Bilkent City Hospital, Ankara, Turkey. perihanperkin@gmail.com.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

|November 3, 2025

概括

强化学习模型显示在黑色素瘤中偏好辅助尼沃卢马布或潘布罗利祖马布,基于优先考虑耐受性或无复发生存. 该模型强调了PD-1抑制剂的场景依赖性治疗权衡.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
临床试验设计临床试验设计

背景情况:

辅助性编程细胞死亡蛋白1 (PD-1) 抑制剂可以改善IIB-IIC阶段黑色素瘤的无复发生存率 (RFS).
没有对比试验直接比较尼沃卢马布和潘布罗利祖马布.
传统的间接比较缺乏对毒性和患者水平的权衡的整合.

研究的目的:

开发一种强化学习 (RL) 框架,用于比较黑色素瘤中的辅助PD-1抑制剂.
模拟在不确定性和竞争的临床优先事项 (疗效与毒性) 下的决策.
为了使治疗权衡明确且取决于场景.

主要方法:

开发了一个RL模型,使用患者状态变量 (年龄,ECOG,阶段,复发时间,不良事件).
行动包括治疗选择 (nivolumab与pembrolizumab).
奖励平衡的RFS收益,对3至4级的AE和停产处罚,使用Q学习进行超过1000个虚拟试验.

主要成果:

来自RL的政策表明有条件的治疗偏好,而不是单一的最佳药物.
在优先考虑耐受性时,由于较低的AE和停药率,Nivolumab更受青.
在优先考虑增量RFS收益时,更喜欢Pembrolizumab;AE建模影响了偏好.

结论:

该RL框架提供了PD-1抑制剂的明确,场景依赖的比较.
它将有效性-毒性平衡置于背景,支持透明的决策.
该模型有助于临床医生和患者导航细微的治疗偏好.

关键词:

辅助疗法是一种辅助疗法.尼沃卢马布的使用情况这是一种PD-1抑制剂.这就是Pembrolizumab.强化学习是一种强化学习.在IIBIIC阶段的黑色素瘤.

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