Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy.

ABSTRACT: The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the validity of patient-specific parameterizations of these models for any particular patient. As a result, sensitivities to these particular parameters can result in therapeutic dosing schedules that are optimal in principle not performing well on particular patients. In this study, we demonstrate that chemotherapeutic dosing strategies learned via reinforcement learning methods are more robust to perturbations in patient-specific parameter values than those learned via classical optimal control methods. By

SUBMITTER: Eastman B 

PROVIDER: S-EPMC8429726 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature