Reinforcement learning strategies in cancer chemotherapy treatments: A review. (February 2023)

• Record Type: Journal Article
• Authors: Yang, Chan-Yun; Shiranthika, Chamani; Wang, Chung-Yih; Chen, Kuo-Wei; Sumathipala, Sagara
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Dynamic treatment regimen -- Chemotherapy -- Reinforcement learning -- Optimal drug schedule; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107280
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Surveying in-depth details in the development of computer aided dynamic treatment regimens (DTR), especially, the DTR for cancer chemotherapy treatment. Collecting new inspirations from reinforcement learning techniques, which are involved or potentially relevant for approaching cancer chemotherapy DTR problem practically. Highlighting potential answers associated with the specific obstacles and open topics in problem domain which reinforcement learning can manage and tackle down. Abstract: 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 mathematical 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 advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning. Results: The insights established in the chemotherapy-planning problem associated with the Reinforcement 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 challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.