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一种基于人工智能的方法来建模放射治疗和免疫治疗之间的协同作用

Hao Peng^1,2, Casey Moore³, Yuanyuan Zhang⁴

¹Departments of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA. hao.peng@utsouthwestern.edu.

Scientific reports

|April 8, 2024

概括

与PD-L1阻断相结合的个性化,超分离型立体自适应性放射治疗 (PULSAR) 显示出协同效应. 一种新的LSTM-RNN模型模拟瘤生长,并揭示了优化癌症治疗策略的因果关系.

科学领域:

在瘤学瘤学.
辐射疗法辐射疗法
免疫治疗是一种免疫疗法.
计算生物学计算生物学

背景情况:

个性化,超分离的立体性适应性放射治疗 (PULSAR) 提供脉冲性瘤杀伤剂量,以延长间隔进行适应性规划和免疫增强.
将放射治疗与免疫治疗结合起来,例如PD-L1阻断,是改善癌症治疗结果的有希望的策略.

研究的目的:

研究PULSAR和PD-L1阻断免疫疗法联合的潜在协同效应.
开发和验证用于模拟结合癌症治疗反应的计算模型.

主要方法:

为了实验数据,利用了易斯肺癌 (LLC) 综基因小鼠癌症模型.
采用长期短期记忆 (LSTM) 循环神经网络 (RNN) 模型来模拟治疗反应.
在LSTM-RNN模型中,处理辐射和抗PD-L1作为连续的外部刺激.

主要成果:

基于时间和辐射剂量,LSTM-RNN模型成功模拟了瘤生长动态.
该模型提供了有关PULSAR和PD-L1阻塞治疗组合的因果关系的机制性见解.
展示了放射治疗和免疫治疗之间的相互作用的新视角.

结论:

开发的in-silico模型可以模拟瘤对联合疗法的反应.
这种方法有助于探索创新的治疗组合,以优化治疗结果.
涉及先进建模和生物标志物识别的进一步研究可以加深对联合治疗机制的理解.

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