Lung cancer is by far the leading cause of cancer death among both men and women. Radiation therapy is one of the main approaches to lung cancer treatment, and its planning is crucial for the therapy outcome. However, the current practice that uniformly delivers the dose does not take into account the patient-specific tumour features that may affect treatment success. Since radiation therapy is by its very nature a sequential procedure, Deep Reinforcement Learning (DRL) is a well-suited methodology to overcome this limitation. In this respect, in this work we present a DRL controller optimizing the daily dose fraction delivered to the patient on the basis of CT scans collected over time during the therapy, offering a personalized treatment not only for volume adaptation, as currently intended, but also for daily fractionation. Furthermore, this contribution introduces a virtual radiotherapy environment based on a set of ordinary differential equations modelling the tissue radiosensitivity by combining both the effect of the radiotherapy treatment and cell growth. Their parameters are estimated from CT scans routinely collected using the Particle Swarm Optimization algorithm. This permits the DRL to learn the optimal behaviour through an iterative trial and error process with the environment. We performed several experiments considering three rewards functions modelling treatment strategies with different tissue aggressiveness and two exploration strategies for the exploration-exploitation dilemma. The results show that our DRL approach can adapt to radiation therapy treatment, optimizing its behaviour according to the different reward functions and outperforming the current clinical practice.

迄今为止，肺癌是男性和女性癌症死亡的主要原因。放射治疗是肺癌治疗的主要方法之一，其计划对治疗结果至关重要。然而，目前统一提供剂量的做法没有考虑可能影响治疗成功的患者特异性肿瘤特征。由于放射治疗本质上是一个顺序程序，因此深度强化学习 (DRL) 是克服这一限制的一种非常合适的方法。在这方面，在这项工作中，我们提出了一个 DRL 控制器，根据治疗期间随时间推移收集的 CT 扫描，优化递送给患者的每日剂量分数，提供个性化的治疗，不仅如当前预期的那样用于容量适应，而且用于每日分馏。此外，这一贡献引入了一个基于一组常微分方程的虚拟放射治疗环境，通过结合放射治疗效果和细胞生长对组织放射敏感性进行建模。它们的参数是从使用粒子群优化算法常规收集的 CT 扫描中估计出来的。这允许 DRL 通过与环境的迭代试错过程来学习最佳行为。我们进行了几次实验，考虑到三种奖励函数建模具有不同组织攻击性的治疗策略和两种探索开发困境的探索策略。结果表明，我们的 DRL 方法可以适应放射治疗，