Reinforcement learning strategies in cancer chemotherapy treatments: A review

Background and objective: Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving math-ematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning.Methods: Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were ad-vantageous to chemotherapy dosing, including both offline reinforcement learning and supervised rein-forcement learning.Results: The insights established in the chemotherapy-planning problem associated with the Reinforce-ment Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques.Conclusions: This study reviewed the recent researches relevant to the topic, and highlighted the chal-lenges, open questions, possible solutions, and future steps in inventing a realistic solution for the afore-mentioned problem.(c) 2022 Elsevier B.V. All rights reserved.