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" View ORCID Profile Kit Gallagher , View ORCID Profile Maximilian Strobl , View ORCID Profile Robert Gatenby , View ORCID Profile Philip Maini , View ORCID Profile 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 type...