Optimal decision making for breast cancer treatment in the presence of cancer regression and type II error in mammography results

Abstract

Breast cancer is the leading cause of cancer death among women worldwide. While breast cancer-screening policies have been widely studied in order to achieve early detection, not much research has been done to optimize treatment decisions once a screening policy is established. In this chapter, we propose a dynamic decision model to determine optimal breast cancer treatment decisions that consider both the impact of overtreatment and the potential delay in cancer detection; these two failures are caused by spontaneous cancer regression and type II error in mammography results, respectively. We measure the impact of medical treatment by means of quality-adjusted life years (QALYs) and our goal is to maximize this metric for a given patient.