Resumen

Breast cancer is the most common cancer among women worldwide. While breast cancer screening policies have been widely studied with the goal to achieve early detection, limited research has been done to optimize treatment decisions once a screening policy is established. In this paper, we propose a dynamic decision model to determine optimal breast cancer treatment decisions that consider both the impact of over-treatment and the potential delay in cancer detection. These two failures are caused by spontaneous cancer regression and type II error in mammography results, respectively. Our goal is to maximize a patient's life score, which depends on various factors: age, cancer stage, estrogen receptor status, type of treatment and the patient's personal opinion about the side effects. Our results indicate that a treatment decision is not always the best option for a patient, and when the decision is to treat the best treatment decision is not always the same. The optimal treatment policy depends on various factors such as age, personal preferences and cancer stage.