Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared to traditional “black-box” DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols.

中文翻译：

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数学模型驱动的深度学习实现个性化自适应治疗

标准护理治疗方案长期以来一直旨在最大程度地杀死细胞，但由于耐药性的出现，这些策略在应用于转移性癌症时往往会失败。适应性治疗策略已被开发为一种替代方法，动态调整治疗以抑制耐药群体的生长，从而延缓甚至阻止肿瘤进展。前列腺癌的有希望的临床结果表明优化适应性治疗方案的潜力。在这里，我们应用深度强化学习（DRL）来指导自适应药物调度，并证明这些治疗方案可以在针对前列腺癌动力学校准的数学模型中优于当前的自适应方案，将进展时间延长一倍以上。 DRL 策略对于患者的变异性（包括肿瘤动态和临床监测计划）具有稳健性。 DRL 框架可以基于单一肿瘤负荷阈值产生可解释的自适应策略，复制并告知最佳治疗策略。 DRL 框架不了解底层的数学肿瘤模型，这证明了 DRL 有助于在新颖或复杂的环境中制定治疗策略的能力。最后，提出的五步路径将机械建模与 DRL 框架相结合，并集成了传统工具，以提高与传统“黑盒”DRL 模型相比的可解释性，可以将该方法转化为临床。总体而言，所提出的框架生成的个性化治疗方案始终优于临床护理标准方案。