Investigating Causal Scope of Radiation Therapy by Uncovering Markov Boundary

Radiation therapy, a cornerstone in cancer treatment, requires precise predictions and personalized treatment plans to increase its efficacy and minimize side effects. However, the relationship between various patient factors and responses to radiation therapy remains unclear, necessitating advanced analytic methods. This study harnesses the Markov boundary method to address this gap. A comprehensive dataset, encompassing patient demographics alongside pre- and post-radiation therapy indices levels, served as the basis for our investigation. Several Markov boundary discovery methods were applied to analyze the Markov boundaries of these factors and their potential interconnections. The primary goal was to reveal unseen relationships that could contribute to the development of more precise treatment predictions and personalized treatment plans. The findings from this study not only aim to enhance our understanding of radiation therapy responses but also to potentially improve patient outcomes through precision medicine.