Advancement and challenges of reinforcement learning in lung cancer imaging

Understanding how reinforcement learning can be applied to lung cancer imaging is essential for progress in this field. Despite increasing interest, there is a clear lack of focused survey papers that explore this intersection. To fill this gap, we conducted a comprehensive review that brings together current research across several key areas. We began by briefly outlining the primary forms of lung cancer to provide context for their imaging needs. Next, we explored RL algorithms that have been specifically adapted for lung cancer imaging tasks. We also reviewed widely used datasets and preprocessing techniques, highlighting their importance in building effective RL-based models. Furthermore, we analyzed recent state-of-the-art studies, focusing on their experimental setups, results, and limitations. This helped us map out the current research landscape. In addition, we identified major technical and practical challenges facing the field today. Based on our findings, we proposed several directions for future research that could address these gaps. Overall, this review offers a structured and in-depth overview of RL applications in lung cancer imaging, covering cancer types, RL models, datasets, preprocessing methods, current trends, open issues, and future prospects.