一种基于生物约束的癌症干细胞动态的基因模型,具有强化学习引导的自适应性放射治疗
Mina Lagzian1, S Ehsan Razavi1, Reyhane Kardehi Moghaddam1
1Department of Electrical Engineering, Ma.C., Islamic Azad University, Mashhad, Iran.
PloS one
|February 5, 2026
概括
癌症干细胞驱动瘤的复发. 一个新的模型模拟瘤生长,并使用人工智能驱动的放射治疗来根据癌症干细胞位置和微环境调整治疗.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
- 人工智能的人工智能是人工智能.
背景情况:
- 癌症干细胞 (CSCs) 对瘤复发和治疗耐药性至关重要.
- 了解固体瘤中CSC的行为对于开发有效的治疗方法至关重要.
研究的目的:
- 开发一种基于生物约束剂的模型 (ABM),模拟从单个CSC的瘤进展.
- 为了整合强化学习 (RL) 适应性放射治疗策略.
主要方法:
- 开发了一种高分辨率的ABM,结合了微环境因素 (氧气,空间限制,迁移,细胞循环).
- 集成了一个Q学习RL组件,用于自适应性辐射剂量调整.
- 模拟瘤发育和CSC动态.
主要成果:
- 该ABM为测试适应性治疗策略提供了一个灵活的环境.
- 集成的RL组件证明了智能处理调整的概念验证可行性.
- 结果表明RL引导的ABM可以个性化放射治疗.
结论:
- 基于生物基的基因模型与强化学习相结合,为适应性,个性化放射治疗提供了一条途径.
- 这种方法可以通过更有效地向癌症干细胞来改善治疗结果.
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