Reinforcement Learning for Optimal Treatment Planning in Radiation Therapy.

Abstract

Radiation therapy stands as a cornerstone in the treatment of cancer, with its efficacy contingent on the precise delivery of therapeutic radiation doses to tumor tissues while minimizing harm to surrounding healthy organs. Conventional treatment planning methods, though effective, often necessitate extensive manual intervention and expert knowledge, [1] limiting their scalability and adaptability. In response, reinforcement learning (RL) has surfaced as a promising paradigm for automating and optimizing treatment planning processes in radiation therapy. This paper presents a comprehensive exploration of the application of RL techniques for achieving optimal treatment planning in radiation therapy. Beginning with an elucidation of the shortcomings of traditional optimization techniques, the study transitions into an exposition of fundamental RL concepts, including states, actions, rewards, and policy optimization. Through this lens, the intricate interplay of RL components in the context of treatment planning is dissected, spanning state representation, action space definition, reward function formulation, modeling approaches, training strategies, and policy refinement. Furthermore, it delves into the nuances of safety and generalization, underscoring the importance of validation and adherence to clinical constraints in RL-based approaches. However, amidst its potential, challenges persist, necessitating a discourse on future directions and opportunities. Through an exploration of these challenges and prospective avenues for research and development, the paper advocates for the continued integration of RL techniques into radiation therapy planning, catalyzing the advancement of personalized cancer treatment modalities.