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Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment

Niazmand, V. R ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1016/j.compbiomed.2024.109041
  3. Publisher: 2024
  4. Abstract:
  5. By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on normalized advantage function reinforcement learning. It aims to boost the body's immune response against cancer cell proliferation. This control approach is applied to provide a combination of both chemotherapy and anti-angiogenic drugs for the first time without the need for complex, predefined mathematical models. It employs a model-free reinforcement learning technique that adaptively adjusts to individual patients to determine optimal drug administration with minimum injection rates. In this regard, a comprehensive and realistic simulation and training environment is employed, with the concentrations of normal cells, cancer cells, and endothelial cells, as well as the levels of chemotherapy and anti-angiogenic agents, as state variables. Furthermore, high levels of disturbances are considered in the simulation to investigate the robustness of the proposed method against probable uncertainties in the treatment process or patient parameters. A practical reward function has also been devised in alignment with medical objectives to ensure effective and safe treatment outcomes. The results demonstrate robustness and superior performance compared to the existing methods. Simulations show that the proposed approach is a dependable strategy for effectively reducing the concentration of cancer cells in the shortest duration using minimal doses of chemotherapy and anti-angiogenic drugs. © 2024 Elsevier Ltd
  6. Keywords:
  7. Anti-angiogenic ; Cancerous tumor ; Chemotherapy ; Angiogenesis inhibitors ; Antineoplastic combined chemotherapy protocols ; Computer simulation ; Deep learning ; Models, biological ; Neoplasm ; Lung cancer ; Angiogenesis inhibitor ; Angiogenesis inhibitor ; Cancer cell ; Immune response ; Normalized advantage function ; Optimal controls ; Reinforcement learning control ; Treatment methods ; Tumor treatment ; Actor critic ; Antiangiogenic therapy ; Cancer combination chemotherapy ; Cancer control ; Cancer patient ; Concentration (parameter) ; Control system ; Deep reinforcement learning ; Drug delivery system ; Drug efficacy ; Drug safety ; Endothelium cell ; Evaluation study ; Extremely randomized tree ; Implementation science ; Learning curve ; Learning environment ; Machine learning ; Malignant neoplasm ; Mathematical model ; Mathematical variable ; Q learning ; Reinforcement learning (machine learning) ; Reward ; Simulation ; Support vector machine ; Treatment outcome ; Uncertainty ; Biological model ; Drug therapy
  8. Source: Computers in Biology and Medicine ; Volume 181 , 2024 ; 00104825 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482524011260