Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer | bioRxiv

Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer | bioRxiv

summary of "Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer" 
Kit Gallagher, Maximilian Strobl, Robert Gatenby, Philip Maini, Alexander Anderson
https://www.biorxiv.org/content/10.1101/2023.04.28.538766v1.full.pdf

The paper proposes a deep reinforcement learning (DRL) approach to optimize personalized cancer treatment for patients with treatment-resistant prostate cancer. The approach uses a combination of DRL algorithms and a biophysical model of prostate cancer growth to iteratively optimize a personalized treatment plan. The study shows that the proposed approach can lead to significant improvements in treatment efficacy compared to standard-of-care treatments. The authors suggest that this DRL approach can be extended to other types of cancer and personalized medicine applications. However, further validation and clinical trials are needed to fully assess the potential of this approach.

 

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