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Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy
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DOI：10.1158/0008-5472.CAN-23-2040.png)
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摘要
En 中文
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 with 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.
Keyword:
PROSTATE-CANCER
EVOLUTIONARY
CHEMOTHERAPY
RESISTANCE
SURVIVAL
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期刊
IF:
16.6
论文数:
10.9W
被引数:
11.9W
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引用论文
Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer深度强化学习识别前列腺癌的个性化间歇性雄激素剥夺治疗OAAI
OAAI分享收藏Evolutionary dynamics of carcinogenesis and why targeted therapy does not work.png)
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