MOEO算法用于多目标优化癌症免疫化疗
K Nozad1, S M Varedi-Koulaei1, M Nazari1
1Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran.
Computers in biology and medicine
|September 6, 2024
概括
一个新的多目标平衡优化算法优化了癌症治疗剂量. 这种方法在标准化疗和混合化疗免疫疗法中有效降低化疗药物剂量,在复杂的治疗策略中表现出优越的性能.
科学领域:
- 计算生物学是一种计算生物学.
- 数学瘤学数学瘤学
- 优化算法的优化算法
背景情况:
- 化疗的非特异性细胞杀伤需要改进癌症治疗策略.
- 化学免疫疗法等混合治疗方法具有潜力,但面临剂量优化挑战.
- 现有的元启发算法在癌症治疗中始终解决复杂的优化问题的局限性.
研究的目的:
- 为优化癌症治疗引入一种新的多目标平衡优化算法 (MOEO).
- 评估MOEO在化疗和化疗免疫治疗方面的表现,考虑到瘤免疫动态和患者健康.
- 为了证明算法的能力,以找到最佳的药物剂量个性化癌症治疗.
主要方法:
- 将单个目标的平衡优化算法扩展到一个多目标版本 (MOEO).
- 应用MOEO以优化化疗和化疗免疫疗法模型中的药物剂量.
- 考虑瘤-免疫动态系统的约束和患者的健康水平.
- 使用两个不同的癌症患者的真实临床数据进行验证.
主要成果:
- MOEO算法成功地确定了化疗和化疗免疫治疗的最佳治疗剂量.
- 对于两位患者:患者1 (138.92,5.84) 和患者2 (16.9,0.4384) 分别进行化疗和化疗免疫治疗,观察到总化疗药物剂量的显著降低.
- 由MOEO生成的帕雷托前线提供了一系列基于不同标准的治疗选择.
结论:
- 拟议的多目标均衡优化器 (MOEO) 算法在优化癌症治疗策略方面表现出卓越的性能.
- 对于化学免疫疗法等混合治疗方法,MOEO特别有效,可降低药物毒性.
- 该算法通过平衡治疗疗效和患者健康,为个性化癌症治疗提供了有价值的工具.
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