Affiliations: [a] Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
| [b] Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Budapest, Hungary
Correspondence:
[*]
Correspondence to: Gábor Kertész. Obuda University Budapest, Hungary. E-mail: [email protected]; [0000-0002-8845-8301].
Abstract: Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches.
Keywords: Tumor growth models, reinforcement learning, reward functions, cancer therapy
